awesome_paper.md

Awesome Transfer Learning Papers

Let's read some awesome transfer learning / domain adaptation papers.

Here, we list some papers by topic. For list by date, please refer to papers by date.

这里收录了迁移学习各个研究领域的最新文章。

• Awesome Transfer Learning Papers
  • Survey
  • Theory
  • Per-training/Finetuning
  • Knowledge distillation
  • Traditional domain adaptation
  • Deep domain adaptation
  • Domain generalization
    • Survey
    • Tutorial
    • Papers
  • Source-free domain adaptation
  • Multi-source domain adaptation
  • Heterogeneous transfer learning
  • Online transfer learning
  • Zero-shot / few-shot learning
  • Multi-task learning
  • Transfer reinforcement learning
  • Transfer metric learning
  • Federated transfer learning
  • Lifelong transfer learning
  • Safe transfer learning
  • Transfer learning applications

Survey

• IEEE TNNLS-22 Towards Personalized Federated Learning

  • A survey on personalized federated learning 一个关于个性化联邦学习的综述

• 2022 Transfer Learning for Future Wireless Networks: A Comprehensive Survey

• 2022 A Review of Deep Transfer Learning and Recent Advancements

• 2022 Transferability in Deep Learning: A Survey

• 2021 Domain generalization: IJCAI-21 Generalizing to Unseen Domains: A Survey on Domain Generalization | 知乎文章 | 微信公众号

  • First survey on domain generalization
  • 第一篇对Domain generalization (领域泛化)的综述

• 2021 Vision-based activity recognition: A Survey of Vision-Based Transfer Learning in Human Activity Recognition

• 2021 ICSAI A State-of-the-Art Survey of Transfer Learning in Structural Health Monitoring

• 2020 Transfer learning: survey and classification, Advances in Intelligent Systems and Computing.

• 2020 迁移学习最新survey，来自中科院计算所庄福振团队，发表在Proceedings of the IEEE: A Comprehensive Survey on Transfer Learning

• 2020 负迁移的综述：Overcoming Negative Transfer: A Survey

• 2020 知识蒸馏的综述: Knowledge Distillation: A Survey

• 用transfer learning进行sentiment classification的综述：A Survey of Sentiment Analysis Based on Transfer Learning

• 2019 一篇新survey：Transfer Adaptation Learning: A Decade Survey

• 2018 一篇迁移度量学习的综述: Transfer Metric Learning: Algorithms, Applications and Outlooks

• 2018 一篇最近的非对称情况下的异构迁移学习综述：Asymmetric Heterogeneous Transfer Learning: A Survey

• 2018 Neural style transfer的一个survey：Neural Style Transfer: A Review

• 2018 深度domain adaptation的一个综述：Deep Visual Domain Adaptation: A Survey

• 2017 多任务学习的综述，来自香港科技大学杨强团队：A survey on multi-task learning

• 2017 异构迁移学习的综述：A survey on heterogeneous transfer learning

• 2017 跨领域数据识别的综述：Cross-dataset recognition: a survey

• 2016 A survey of transfer learning。其中交代了一些比较经典的如同构、异构等学习方法代表性文章。

• 2015 中文综述：迁移学习研究进展

• 2010 A survey on transfer learning

• Survey on applications - 应用导向的综述：

  • 视觉domain adaptation综述：Visual Domain Adaptation: A Survey of Recent Advances
  • 迁移学习应用于行为识别综述：Transfer Learning for Activity Recognition: A Survey
  • 迁移学习与增强学习：Transfer Learning for Reinforcement Learning Domains: A Survey
  • 多个源域进行迁移的综述：A Survey of Multi-source Domain Adaptation。

• Data-Free Knowledge Transfer: A Survey

  • A survey on data-free distillation and source-free DA
  • 一篇关于data-free蒸馏和source-free DA的综述

Theory

• Improved OOD Generalization via Conditional Invariant Regularizer

  • Improved OOD generalization via conditional invariant regularizer 通过条件不变正则进行OOD泛化

• An Information-Theoretic Analysis for Transfer Learning: Error Bounds and Applications

  • Information-theoretic analysis for transfer learning 用信息理论解释迁移学习

• PAC-Bayesian Domain Adaptation Bounds for Multiclass Learners

  • PAC-Bayesian domain adaptation 基于PAC-Bayesian的domain adaptation

• Optimal Representations for Covariate Shift

  • Learning optimal representations for covariate shift
  • 为covariate shift学习最优的表达

• NeurIPS-21 On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources

  • Theory and algorithm of domain-invariant learning for transfer learning
  • 对invariant representation的理论和算法

• 20210625 ICML-21 f-Domain-Adversarial Learning: Theory and Algorithms

  • New theory based on f-divergence
  • 基于f-divergence给出新的DA理论和算法

• 20210521 When is invariance useful in an Out-of-Distribution Generalization problem ?

  • When is invariant useful in OOD?
  • 理论上分析了在OOD问题中invariance什么时候有用

• 20200220 Butterfly: One-step Approach towards Wildly Unsupervised Domain Adaptation

  • Noisy domain adaptation
  • 用于噪声环境中的domain adaptation的方法

• 20210127 A Unified Joint Maximum Mean Discrepancy for Domain Adaptation

  • 一个理论上更一般化的MMD差异用于领域自适应
  • A more general MMD for domain adaptation

• 20200615 Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks

• 20200813 A Boundary Based Out-of-Distribution Classifier for Generalized Zero-Shot Learning

  • OOD classifier for generalized zero-shot learning

• 20200813 ICML-20 On Learning Language-Invariant Representations for Universal Machine Translation

  • Theory for universal machine translation
  • 对统一机器翻译模型进行了理论论证

• 20200702 ICML-20 Few-shot domain adaptation by causal mechanism transfer

  • The first work on causal transfer learning
  • 日本理论组大佬Sugiyama的工作，causal transfer learning

• 20191008 CVPR-19 Characterizing and Avoiding Negative Transfer

  • Characterizing and avoid negative transfer
  • 形式化并提出如何避免负迁移

• 20190301 ALT-19 A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation

  • A new criterion for domain adaptation
  • 提出一种新的可以强化domain adaptation表现的度量

• 20181219 arXiv PAC Learning Guarantees Under Covariate Shift

  • PAC learning theory for covariate shift
  • Covariate shift问题的PAC学习理论

• 20181206 arXiv Transferring Knowledge across Learning Processes

  • Transfer learning across learning processes
  • 学习过程中的知识迁移

• 20181128 arXiv Theoretical Guarantees of Transfer Learning

  • Some theoretical analysis of transfer learning
  • 一些关于迁移学习的理论分析

• 20181117 arXiv Theoretical Perspective of Deep Domain Adaptation

  • Providing some theory analysis on deep domain adaptation
  • 对deep domain adaptaiton做出了一些理论上的分析

• 20181106 workshop GENERALIZATION BOUNDS FOR DOMAIN ADAPTATION VIA DOMAIN TRANSFORMATIONS

  • Analyze some generalization bound for domain adaptation
  • 对domain adaptation进行了一些理论上的分析

• 20180724 arXiv Generalization Bounds for Unsupervised Cross-Domain Mapping with WGANs

  • Provide a generalization bound for unsupervised WGAN in transfer learning
  • 对迁移学习中无监督的WGAN进行了一些理论上的分析

Per-training/Finetuning

• Parameter-Efficient Tuning Makes a Good Classification Head [arxiv]

  • Parameter-efficient tuning makes a good classification head 参数高效的迁移学习成就一个好的分类头

• CVPR'23 Trainable Projected Gradient Method for Robust Fine-tuning [arxiv]

  • Trainable PGD for robust fine-tuning 可训练的pgd用于鲁棒的微调技术

• ICLR'23 Contrastive Alignment of Vision to Language Through Parameter-Efficient Transfer Learning [arxiv]

  • Contrastive alignment for vision language models using transfer learning 使用参数高效迁移进行视觉语言模型的对比对齐

• ICLR'23 workshop SPDF: Sparse Pre-training and Dense Fine-tuning for Large Language Models [arxiv]

  • Sparse pre-training and dense fine-tuning

• A Unified Continual Learning Framework with General Parameter-Efficient Tuning [arxiv]

  • A continual learning framework for parameter-efficient tuning 一个对于参数高效迁移的连续学习框架

• Transfer Learning for Real-time Deployment of a Screening Tool for Depression Detection Using Actigraphy [arxiv]

  • Transfer learning for Depression detection 迁移学习用于脉动计焦虑检测

• ICLR'23 AutoTransfer: AutoML with Knowledge Transfer -- An Application to Graph Neural Networks [arxiv]

  • GNN with autoML transfer learning 用于GNN的自动迁移学习

• Revisit Parameter-Efficient Transfer Learning: A Two-Stage Paradigm [arxiv]

  • Parameter-efficient transfer learning: a two-stage approach 一种两阶段的参数高效迁移学习

• To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning [arxiv]

  • Ensembling in transfer learning 调研迁移学习中的集成

• CVPR'13 Masked Images Are Counterfactual Samples for Robust Fine-tuning [arxiv]

  • Masked images for robust fine-tuning 调研masked image对于fine-tuning的影响

• Finetune like you pretrain: Improved finetuning of zero-shot vision models [arxiv]]

  • Improved fine-tuning of zero-shot models 针对zero-shot model提高fine-tuneing

• CVPR'22 Does Robustness on ImageNet Transfer to Downstream Tasks? [arxiv]

  • Does robustness on imagenet transfer lto downstream tasks?

• NeurIPS'22 Improved Fine-Tuning by Better Leveraging Pre-Training Data [openreview]

  • Using pre-training data for fine-tuning 用预训练数据来做微调

• On Fine-Tuned Deep Features for Unsupervised Domain Adaptation [arxiv]

  • Fine-tuned features for domain adaptation 微调的特征用于域自适应

• Transfer of Machine Learning Fairness across Domains [arxiv]

  • Fairness transfer in transfer learning 迁移学习中的公平性迁移

• CVPR-20 Regularizing CNN Transfer Learning With Randomised Regression [arxiv]

  • Using randomized regression to regularize CNN 用随机回归约束CNN迁移学习

• AAAI-21 TransTailor: Pruning the Pre-trained Model for Improved Transfer Learning [arxiv]

  • Pruning pre-trained model for transfer learning 通过对预训练模型进行剪枝来进行迁移学习

• Test-Time Training with Masked Autoencoders [arxiv]

  • Test-time training with MAE MAE的测试时训练

• Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models [arxiv]

  • Test-time prompt tuning 测试时的prompt tuning

• TeST: test-time self-training under distribution shift [arxiv]

  • Test-time self-training 测试时的self-training

• Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets

  • Parameter efficient CNN adapter for transfer learning 参数高效的CNN adapter用于迁移学习

• Hyper-Representations for Pre-Training and Transfer Learning

  • Hyper-representation for pre-training and fine-tuning 对于预训练和微调的超表示

• Zero-Shot AutoML with Pretrained Models

  • 用预训练模型进行零样本的自动机器学习

• How robust are pre-trained models to distribution shift?

  • How robust are pre-trained models to distribution shift 评估预训练模型对于distribution shift的鲁棒性

• Wav2vec-S: Semi-Supervised Pre-Training for Speech Recognition

  • Pretraining for speech recognition 用预训练模型进行语音识别

• ScholarBERT: Bigger is Not Always Better

  • Empirical study on fine-tuning experiments 提出ScholarBERT进行大规模finetuning实验

• A Domain-adaptive Pre-training Approach for Language Bias Detection in News

  • Domain-adaptive pre-training for language bias detection 领域适配预训练用于新闻语言偏见检测

• IJCAI-22 Parameter-Efficient Sparsity for Large Language Models Fine-Tuning

  • Parameter-efficient sparsity for language model fine-tuning 参数高效的稀疏学习用于语言模型微调

• NAACL-22 Efficient Few-Shot Fine-Tuning for Opinion Summarization

  • Few-shot fine-tuning for opinion summarization 小样本微调技术用于评论总结

• ACL-22 Probing Simile Knowledge from Pre-trained Language Models

  • Probe simile knowledge from pre-trained model 从预训练模型中找出明喻知识

• Transfer Learning with Pre-trained Conditional Generative Models

  • Transfer learning with pre-trained conditional generative models 条件生成模型用于迁移学习

• ICLR-22 Towards a Unified View of Parameter-Efficient Transfer Learning

  • Unified view of parameter-efficient transfer learning 一个统一视角看待参数高效的迁移学习

• ICLR-22 Exploring the Limits of Large Scale Pre-training

  • Many experiments to explore pre-training 许多实验来探索预训练

• Just Fine-tune Twice: Selective Differential Privacy for Large Language Models

  • Differential privacy by just fine-tune twice 通过微调两次进行差分隐私

• On Effectively Learning of Knowledge in Continual Pre-training

  • Continual per-training 持续的预训练

• NAACL-22 GRAM: Fast Fine-tuning of Pre-trained Language Models for Content-based Collaborative Filtering

  • Fast fine-tuning for content-based collaborative filtering
  • 快速的适用于协同过滤的微调

• AAAI-22 Powering Finetuning in Few-shot Learning: Domain-Agnostic Feature Adaptation with Rectified Class Prototypes

  • Finetuning in few-shot learning
  • 小样本学习中的微调

• CVPR-22 Does Robustness on ImageNet Transfer to Downstream Tasks?

  • Transfer learning robustness
  • 迁移学习鲁棒性

• Blockchain as an Enabler for Transfer Learning in Smart Environments

  • Blockchain transfer learning
  • 用区块链进行迁移学习

• ICLR-22 Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution

  • Fin-tuning and linear probing for ood generalization
  • 先linear probing最后一层再finetune对OOD任务最好

• A Broad Study of Pre-training for Domain Generalization and Adaptation

  • A broad study of pre-training models for DA and DG
  • 大量的实验进行DA和DG

• ACL-22 Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with Articulatory Features

  • Language-agnostic meta-learning for TTS
  • 语言无关的元学习用于TTS

• Input-Tuning: Adapting Unfamiliar Inputs to Frozen Pretrained Models

  • Adapt unfamiliar inputs to frozen pretrained models
  • 让固定的预训练模型适配不熟悉的输入

• Pre-trained Token-replaced Detection Model as Few-shot Learner

  • Pre-trained token-replaced detection model as few-shot learner
  • 预训练的替换token的检测模型

• ICLR-22 spotlight Towards a Unified View of Parameter-Efficient Transfer Learning

  • Detailed analysis of parameter-efficient transfer learning
  • 对参数高效的迁移学习进行分析

• ICLR-22 BEiT: BERT Pre-Training of Image Transformers

  • BERT pre-training of image transformers
  • 用BERT的方式pre-train transformer

• Improved Fine-tuning by Leveraging Pre-training Data: Theory and Practice

  • Using pre-training data to improve fine-tuning
  • 使用预训练数据来帮助finetune

• An Ensemble of Pre-trained Transformer Models For Imbalanced Multiclass Malware Classification

  • An ensemble of pre-trained transformer for malware classification
  • 预训练的transformer通过集成进行恶意软件检测

• SLIP: Self-supervision meets Language-Image Pre-training

  • Self-supervised learning + language image pretraining
  • 用自监督学习用于语言到图像的预训练

• VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks

  • Vision-language efficient transfer learning
  • 参数高校的vision-language任务迁移

• Revisiting the Transferability of Supervised Pretraining: an MLP Perspective

  • Revisit the transferability of supervised pretraining
  • 重新思考有监督预训练的可迁移性

• NeurIPS-21 workshop Maximum Mean Discrepancy for Generalization in the Presence of Distribution and Missingness Shift

  • MMD for covariate shift
  • 用MMD来解决covariate shift问题

• Combined Scaling for Zero-shot Transfer Learning

  • Scaling up for zero-shot transfer learning
  • 增大训练规模用于zero-shot迁移学习

• Improved Regularization and Robustness for Fine-tuning in Neural Networks

  • Improve regularization and robustness for finetuning
  • 针对finetune提高其正则和鲁棒性

• NeurIPS-21 Modular Gaussian Processes for Transfer Learning

  • Modular Gaussian process for transfer learning
  • 在迁移学习中使用modular Gaussian过程

• Rethinking supervised pre-training for better downstream transferring

  • Rethink better finetune
  • 重新思考预训练以便更好finetune

• EMNLP-21 Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning

  • Few-shot intent detection using pretrain and finetune
  • 用迁移学习进行少样本意图检测

• KroneckerBERT: Learning Kronecker Decomposition for Pre-trained Language Models via Knowledge Distillation

  • Using Kronecker decomposition and knowledge distillation for pre-trained language models compression
  • 用Kronecker分解和知识蒸馏来进行语言模型的压缩

• How Does Adversarial Fine-Tuning Benefit BERT?

  • Examine how does adversarial fine-tuning help BERT
  • 探索对抗性finetune如何帮助BERT

• A Data Augmented Approach to Transfer Learning for Covid-19 Detection

  • Data augmentation to transfer learning for COVID
  • 迁移学习使用数据增强，用于COVID-19

• Finetuning Pretrained Transformers into Variational Autoencoders

  • Finetune transformer to VAE
  • 把transformer迁移到VAE

• Pre-trained Models for Sonar Images

  • Pre-trained models for sonar images
  • 针对声纳图像的预训练模型

• Domain Adaptor Networks for Hyperspectral Image Recognition

  • Finetune for hyperspectral image recognition
  • 针对高光谱图像识别的迁移学习

• CVPR-21 Efficient Conditional GAN Transfer With Knowledge Propagation Across Classes

  • Transfer conditional GANs to unseen classes
  • 通过知识传递，迁移预训练的conditional GAN到新类别

• 20191011 arXiv Estimating Transfer Entropy via Copula Entropy

  • Evaluate the transfer entopy via copula entropy
  • 评估迁移熵

• 20180925 arXiv DT-LET: Deep Transfer Learning by Exploring where to Transfer

  • Explore the suitable layers to transfer
  • 探索深度网络中效果表现好的对应的迁移层

• 20200629 Transfer learning via L1 regulaziation

  • Using L1 regularizationg for transfer learning

• 20201016 Deep Ensembles for Low-Data Transfer Learning

  • Deep ensemble models for transfer learning

• 20210511 ACL-21 Are Pre-trained Convolutions Better than Pre-trained Transformers?

  • Empirically investigate pre-trained convolutions and Transformers
  • 设计实验探索预训练的卷积和Transformer的对比

• 20190111 arXiv Transfer Representation Learning with TSK Fuzzy System

  • Transfer learning with fuzzy system
  • 基于模糊系统的迁移学习

• 20201203 Pre-Trained Image Processing Transformer

  • 用transformer做low-level的图像任务

• 20200821 Self-Supervised Learning Across Domains

  • 跨领域自监督学习

• 20180403 arXiv 选择最优的子类生成方便迁移的特征：Class Subset Selection for Transfer Learning using Submodularity

• 20180326 ICMLA-17 在类别不平衡情况下比较了一些迁移学习和传统方法的性能，并做出一些结论：Comparing Transfer Learning and Traditional Learning Under Domain Class Imbalance

• 20190626 arXiv Transfer of Machine Learning Fairness across Domains

  • Transfer of machine learning fairness across domains
  • 机器学习的公平性的迁移

• 20200615 Rethinking Pre-training and Self-training

• 20200210 WACVW-20 Impact of ImageNet Model Selection on Domain Adaptation

  • A good experiment paper to indicate the power of representations
  • 一篇很好的实验paper，揭示了深度特征+传统方法的有效性

• 20191015 arXiv The Visual Task Adaptation Benchmark

  • A new large benchmark for visual adaptation tasks by Google
  • Google提出的一个巨大的视觉迁移任务数据集

• 20190305 arXiv Let's Transfer Transformations of Shared Semantic Representations

  • Transfer transformations from shared semantic representations
  • 从共享的语义表示中进行特征迁移

• 20190409 arXiv Improving Image Classification Robustness through Selective CNN-Filters Fine-Tuning

  • Improving Image Classification Robustness through Selective CNN-Filters Fine-Tuning
  • 通过可选择的CNN滤波器进行图像分类的fine-tuning

• 20190111 arXiv Low-Cost Transfer Learning of Face Tasks

  • Infer what task transfers better and how to transfer
  • 探索对于一个预训练好的网络来说哪个任务适合迁移、如何迁移

• 20191119 ICDM-19 Towards Making Deep Transfer Learning Never Hurt

  • Towards making deep transfer learning never hurt
  • 通过正则避免负迁移

• 20181123 arXiv SpotTune: Transfer Learning through Adaptive Fine-tuning

  • Very interesting work: how exactly determine the finetune process?
  • 很有意思的工作：如何决定finetune的策略？

• 20180425 arXiv 探索各个层对于迁移任务的作用，方便以后的迁移。比较有意思：CactusNets: Layer Applicability as a Metric for Transfer Learning

• 传递迁移学习的第一篇文章，来自杨强团队，发表在KDD-15上：Transitive Transfer Learning

• AAAI-17 杨强团队最新的传递迁移学习：Distant Domain Transfer Learning

• 20180819 LNCS-2018 Distant Domain Adaptation for Text Classification

  • Propose a selected algorithm for distant domain text classification
  • 提出一个用于远域的文本分类方法

• 20190220 arXiv Fully-Featured Attribute Transfer

  • Fully-featured image attribute transfer
  • 图像特征迁移

• 20190926 arXiv Transfer Learning across Languages from Someone Else's NMT Model

  • Transfer learning across languages from NMT pretrained model
  • 利用预训练好的NMT模型进行迁移学习

• 20190929 NeurIPS-19 Deep Model Transferability from Attribution Maps

  • Using attribution map for network similarity
  • 与cvpr18的taskmony类似，这次用了属性图的方式探索网络的相似性

• 20181121 arXiv An Efficient Transfer Learning Technique by Using Final Fully-Connected Layer Output Features of Deep Networks

  • Using final fc layer to perform transfer learning
  • 使用最后一层全连接层进行迁移学习

• ICML-14 著名的DeCAF特征：DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

• Simultaneous Deep Transfer Across Domains and Tasks

  • 发表在ICCV-15上，在传统深度迁移方法上又加了新东西
  • 我的解读

Knowledge distillation

• NeurIPS'22 Respecting Transfer Gap in Knowledge Distillation [arxiv]

  • Transfer gap in distillation 知识蒸馏中的迁移gap

• Cross-Architecture Knowledge Distillation

  • Cross-architecture knowledge distillation 跨架构的知识蒸馏

• ECCV-22 Knowledge Condensation Distillation

  • Knowledge condensation distillation 知识压缩蒸馏

• FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning

  • Self-adaptive thresholding for semi-supervised learning 新的自适应阈值半监督方法

• TIP-22 Spot-adaptive Knowledge Distillation

  • Spot-adaptive knowledge distillation 层次自适应的知识蒸馏

• CVPR-22 Decoupled Knowledge Distillation

  • Decoupled knowledge distillation
  • 解耦的知识蒸馏

• On Representation Knowledge Distillation for Graph Neural Networks

  • Knowledge distillation for GNN
  • 适用于GNN的知识蒸馏

• Estimating and Maximizing Mutual Information for Knowledge Distillation

  • Global and local mutual information maximation for knowledge distillation
  • 局部和全局互信息最大化用于蒸馏

• 20210426 Distill on the Go: Online knowledge distillation in self-supervised learning

  • Online knowledge distillation in self-supervised learning
  • 自监督学习中的在线知识蒸馏

• 20210202 ICLR-21 Rethinking Soft Labels for Knowledge Distillation: A Bias-Variance Tradeoff Perspective

  • Rethink soft labels for KD in a bias-variance tradeoff perspective
  • 从偏差-方差的角度重新思考蒸馏中的软标签

• 20200706 Interactive Knowledge Distillation

• 20200412 ICML-19 Towards understanding knowledge distillation

  • Some theoretical and empirical understanding to knowledge distllation
  • 对知识蒸馏的一些理论和实验的分析

• 20191202 AAAI-20 Towards Oracle Knowledge Distillation with Neural Architecture Search

  • Using NAS for knowledge Distillation
  • 用NAS帮助知识蒸馏

• 20190401 arXiv Distilling Task-Specific Knowledge from BERT into Simple Neural Networks

  • Distill knowledge from BERT to simple neural networks
  • 从BERT模型中迁移知识到简单网络中

• 20181207 arXiv Feature Matters: A Stage-by-Stage Approach for Knowledge Transfer

  • Feature transfer in student-teacher networks
  • 在学生-教师网络中进行特征迁移

• 20191204 AAAI-20 Online Knowledge Distillation with Diverse Peers

  • Online Knowledge Distillation with Diverse Peers

• 20191222 AAAI-20 Improved Knowledge Distillation via Teacher Assistant

  • Teacher assistant helps knowledge distillation

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Traditional domain adaptation

• On Label Shift in Domain Adaptation via Wasserstein Distance

  • Using Wasserstein distance to solve label shift in domain adaptation
  • 在DA领域中用Wasserstein distance去解决label shift问题

• 20210319 Cross-domain Activity Recognition via Substructural Optimal Transport | 知乎文章 | 微信公众号

  • Using sub-structures for domain adaptation
  • 采用子结构进行domain adaptation，比传统方法快5倍

• 20210607 ICML-21 Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts

  • Sequential DA using distributionally robust experts
  • 用鲁棒专家模型进行连续式领域自适应

• 20200615 Deep Transfer Learning with Ridge Regression

• 20200324 Domain Adaptation by Class Centroid Matching and Local Manifold Self-Learning

  • Domain adaptation by class centroid matching and local manifold self-learning
  • 集合了聚类、中心匹配，及自学习的DA

• 20191204 arXiv Transferability versus Discriminability: Joint Probability Distribution Adaptation (JPDA)

  • Joint adaptation with different weights
  • 不同权重的联合概率适配

• 20191125 AAAI-20 Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling

  • DA with selective pseudo label
  • 结构化和选择性的伪标签用于DA

• 20190703 arXiv Domain Adaptation via Low-Rank Basis Approximation

  • Domain adaptation with low-rank basis approximation
  • 低秩分解进行domain adaptation

• 20190508 IJCNN-19 Unsupervised Domain Adaptation using Graph Transduction Games

  • Domain adaptation using graph transduction games
  • 用图转换博弈进行domain adaptation

• 20190403 ICME-19 Easy Transfer Learning By Exploiting Intra-domain Structures Code

  • An easy transfer learning approach with good performance
  • 一个非常简单但效果很好的迁移方法

• 20180724 ACMMM-18 Visual Domain Adaptation with Manifold Embedded Distribution Alignment

  • The state-of-the-art results of domain adaptation, better than most traditional and deep methods
  • 目前效果最好的非深度迁移学习方法，领先绝大多数最近的方法
  • Code: MEDA

• 20180912 arXiv Unsupervised Domain Adaptation Based on Source-guided Discrepancy

  • Using source domain information to help domain adaptation
  • 使用源领域数据辅助目标领域进行domain adaptation

• 20181219 arXiv Domain Adaptation on Graphs by Learning Graph Topologies: Theoretical Analysis and an Algorithm

  • Domain adaptation on graphs
  • 在图上的领域自适应

• 20181121 arXiv Deep Discriminative Learning for Unsupervised Domain Adaptation

  • Deep discriminative learning for domain adaptation
  • 同时进行源域和目标域上的分类判别

• 20181114 arXiv Multiple Subspace Alignment Improves Domain Adaptation

  • Project domains into multiple subsapce to do domain adaptation
  • 将domain映射到多个subsapce上然后进行adaptation

• 20180912 ICIP-18 Structural Domain Adaptation with Latent Graph Alignment

  • Using graph alignment for domain adaptation
  • 使用图对齐方式进行domain adaptation

• 20180912 IEEE Access Unsupervised Domain Adaptation by Mapped Correlation Alignment

  • Mapped correlation alignment for domain adaptation
  • 用映射的关联对齐进行domain adaptation

• 20180912 ICALIP-18 Domain Adaptation for Gaussian Process Classification

  • Domain Adaptation for Gaussian Process Classification
  • 在高斯过程分类中进行domain adaptation

• 20180701 arXiv 对domain adaptation问题，基于optimal transport提出一种新的特征选择方法：Feature Selection for Unsupervised Domain Adaptation using Optimal Transport

• 20180510 IEEE Trans. Cybernetics 提出一个通用的迁移学习框架，对不同的domain进行不同的特征变换：Transfer Independently Together: A Generalized Framework for Domain Adaptation

• 20180403 TIP-18 一篇传统方法做domain adaptation的文章，比很多深度方法结果都好：An Embarrassingly Simple Approach to Visual Domain Adaptation

• 20180326 ICMLA-17 利用subsapce alignment进行迁移学习：Transfer Learning for Large Scale Data Using Subspace Alignment

• 20180228 arXiv 一篇通过标签一致性和MMD准则进行domain adaptation的文章: Discriminative Label Consistent Domain Adaptation

• 20180226 AAAI-18 清华龙明盛组最新工作：Unsupervised Domain Adaptation with Distribution Matching Machines

• 20180110 arXiv 一篇比较新的传统方法做domain adaptation的文章 Close Yet Discriminative Domain Adaptation

• 20180105 arXiv 最优的贝叶斯迁移学习 Optimal Bayesian Transfer Learning

• 20171201 ICCV-17 When Unsupervised Domain Adaptation Meets Tensor Representations

  • 第一篇将Tensor与domain adaptation结合的文章。代码
  • 我的解读

• 201711 ICCV-17 Open set domain adaptation。

  • 当source和target只共享某一些类别时，怎么处理？这个文章获得了ICCV 2017的Marr Prize Honorable Mention，值得好好研究。
  • 我的解读

• 201710 Domain Adaptation in Computer Vision Applications 里面收录了若干篇domain adaptation的文章，可以大概看看。

• 学习迁移(Learning to Transfer, L2T)

  • 迁移学习领域的新方向：与在线、增量学习结合
  • 我的解读

• 201707 Mutual Alignment Transfer Learning

• 201708 Learning Invariant Riemannian Geometric Representations Using Deep Nets

• 20170812 ICML-18 Learning To Transfer，将迁移学习和增量学习的思想结合起来，为迁移学习的发展开辟了一个崭新的研究方向。我的解读

• NIPS-17 JDOT: Joint distribution optimal transportation for domain adaptation

• AAAI-16 Return of Frustratingly Easy Domain Adaptation

• JMLR-16 Distribution-Matching Embedding for Visual Domain Adaptation

• CoRR abs/1610.04420 (2016) Theoretical Analysis of Domain Adaptation with Optimal Transport

• CVPR-14 Transfer Joint Matching for Unsupervised Domain Adaptation

• ICCV-13 Transfer Feature Learning with Joint Distribution Adaptation

• 迁移成分分析方法(Transfer component analysis, TCA)

  • Domain adaptation via tranfer component analysis
  • 发表在IEEE Trans. Neural Network期刊上（现改名为IEEE trans. Neural Network and Learning System），前作会议文章发在AAAI-09上
  • 我的解读

• 联合分布适配方法（joint distribution adaptation，JDA）

  • Transfer Feature Learning with Joint Distribution Adaptation
  • 发表在2013年的ICCV上
  • 我的解读

• 测地线流式核方法(Geodesic flow kernel, GFK)

  • Geodesic flow kernel for unsupervised domain adaptation
  • 发表在CVPR-12上
  • 我的解读

• 领域不变性迁移核学习(Transfer Kernel Learning, TKL)

  • Domain invariant transfer kernel learning
  • 发表在IEEE Trans. Knowledge and Data Engineering期刊上

Deep domain adaptation

• Complementary Domain Adaptation and Generalization for Unsupervised Continual Domain Shift Learning [arxiv]

  • Continual domain shift learning using adaptation and generalization 使用 adaptation和DG进行持续分布变化的学习

• CVPR'23 Feature Alignment and Uniformity for Test Time Adaptation [arxiv]

  • Feature alignment for test-time adaptation 使用特征对齐进行测试时adaptation

• TempT: Temporal consistency for Test-time adaptation [arxiv]

  • Temporeal consistency for test-time adaptation 时间一致性用于test-time adaptation

• CVPR'23 A New Benchmark: On the Utility of Synthetic Data with Blender for Bare Supervised Learning and Downstream Domain Adaptation [arxiv]

  • A new benchmark for domain adaptation 一个对于domain adaptation最新的benchmark

• Unsupervised domain adaptation by learning using privileged information [arxiv]

  • Domain adaptation by privileged information 使用高级信息进行domain adaptation

• Probabilistic Domain Adaptation for Biomedical Image Segmentation [arxiv]

  • Probabilistic domain adaptation for biomedical image segmentation 概率的domain adaptation用于生物医疗图像分割

• Unsupervised Cumulative Domain Adaptation for Foggy Scene Optical Flow [arxiv]

  • Domain adaptation for foggy scene optical flow 领域自适应用于雾场景的光流

• Domain Adaptation for Time Series Under Feature and Label Shifts [arxiv]

  • Domain adaptation for time series 用于时间序列的domain adaptation

• TPAMI'23 Source-Free Unsupervised Domain Adaptation: A Survey [arxiv]

  • A survey on source-free domain adaptation 关于source-free DA的一个最新综述

• Discriminative Radial Domain Adaptation [arxiv]

  • Discriminative radial domain adaptation 判别性的放射式domain adaptation

• WACV'23 Cross-Domain Video Anomaly Detection without Target Domain Adaptation [arxiv]

  • Cross-domain video anomaly detection without target domain adaptation 跨域视频异常检测

• Co-Learning with Pre-Trained Networks Improves Source-Free Domain Adaptation [arxiv]

  • Pre-trained models for source-free domain adaptation 用预训练模型进行source-free DA

• CONDA: Continual Unsupervised Domain Adaptation Learning in Visual Perception for Self-Driving Cars [arxiv]

  • Continual DA for self-driving cars 连续的domain adaptation用于自动驾驶

• Robust Mean Teacher for Continual and Gradual Test-Time Adaptation [arxiv]

  • Mean teacher for test-time adaptation 在测试时用mean teacher进行适配

• ECCV-22 DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation [arXiv] [Code]

  • Domain adaptation in semantic segmentation 语义分割域适应

• NeurIPS'22 Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning [openreview]

  • Adaptive contrastive learning for source-free DA 自适应的对比学习用于source-free DA

• NeurIPS'22 MetaTeacher: Coordinating Multi-Model Domain Adaptation for Medical Image Classification [openreview]

  • Multi-model domain adaptation mor medical image classification 多模型DA用于医疗数据

• NeurIPS'22 Domain Adaptation under Open Set Label Shift [openreview]

  • Domain adaptation under open set label shift 在开放集的label shift中的DA

• NeurIPS'22 Test Time Adaptation via Conjugate Pseudo-labels [openreview]

  • Test-time adaptation with conjugate pseudo-labels 用伪标签进行测试时adaptation

• On Fine-Tuned Deep Features for Unsupervised Domain Adaptation [arxiv]

  • Fine-tuned features for domain adaptation 微调的特征用于域自适应

• WACV-23 ConfMix: Unsupervised Domain Adaptation for Object Detection via Confidence-based Mixing [arxiv]

  • Domain adaptation for object detection using confidence mixing 用置信度mix做domain adaptation

• Unsupervised Domain Adaptation for COVID-19 Information Service with Contrastive Adversarial Domain Mixup [arxiv]

  • Domain adaptation for COVID-19 用DA进行COVID-19预测

• ICONIP'22 IDPL: Intra-subdomain adaptation adversarial learning segmentation method based on Dynamic Pseudo Labels [arxiv]

  • Intra-domain adaptation for segmentation 子领域对抗Adaptation

• NeurIPS'22 Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks [arxiv]

  • Parameter-efficient multi-task adaptation 参数高效的多任务adaptation

• Deep Domain Adaptation for Detecting Bomb Craters in Aerial Images [arxiv]

  • Bomb craters detection using domain adaptation 用DA检测遥感图像中的炮弹弹坑

• WACV-23 TeST: Test-time Self-Training under Distribution Shift [arxiv]

  • Test-time self-training 测试时训练

• Robust Domain Adaptation for Machine Reading Comprehension [arxiv]

  • Domain adaptation for machine reading comprehension 机器阅读理解的domain adaptation

• IEEE-TMM'22 Uncertainty Modeling for Robust Domain Adaptation Under Noisy Environments [IEEE]

  • Uncertainty modeling for domain adaptation 噪声环境下的domain adaptation

• MM-22 Making the Best of Both Worlds: A Domain-Oriented Transformer for Unsupervised Domain Adaptation

  • Transformer for domain adaptation 用transformer进行DA

• NeurIPS-21 The balancing principle for parameter choice in distance-regularized domain adaptation

  • Hyperparameter selection for domain adaptation 对adaptation中的正则项系数进行选择

• ECCV-22 Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptation

  • Prototype continual domain adaptation 基于原型的类增量domain adaptation

• Transferability-Guided Cross-Domain Cross-Task Transfer Learning

  • Cross-domain cross-task transfer learning 用迁移性指标指导跨领域跨任务迁移

• A Data-Based Perspective on Transfer Learning

  • Analyze the data numbers in transfer learning 分析迁移学习中数据的重要性

• Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation Learning

  • Few-shot DA for unsupervised constrastive learning 小样本DA用于无监督对比学习

• ICPR-22 OTAdapt: Optimal Transport-based Approach For Unsupervised Domain Adaptation

  • Optimal transport-based domain adaptation 利用最优传输进行领域自适应

• CVPR-22 Safe Self-Refinement for Transformer-based Domain Adaptation

  • Transformer-based domain adaptation 基于transformer的domain adaptation

• ISPASS-22 Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices

  • Benchmarking test-time adaptation for edge devices
  • 在端设备上评测test-time adaptation算法

• Multi-Source Domain Adaptation Based on Federated Knowledge Alignment

  • Multi-source domain adaptation
  • 多源域自适应

• A Broad Study of Pre-training for Domain Generalization and Adaptation

  • A broad study of pre-training models for DA and DG
  • 大量的实验进行DA和DG

• Open Set Domain Adaptation By Novel Class Discovery

  • Open set DA by novel class discovery
  • 基于新类发现的open set da

• ICML-21 workshop Domain Adaptation with Factorizable Joint Shift

  • Domain adaptation with factorizable joint shift
  • 基于可分解的联合漂移的领域自适应

• Causal Domain Adaptation with Copula Entropy based Conditional Independence Test

  • Use copula entropy based conditional independence test for csusal domain adaptation
  • 使用基于copula entopy的条件独立测试进行causal domain adaptation

• ICLR-22 Graph-Relational Domain Adaptation

  • Graph-relational domain adapttion using topological structures
  • 图级别的domain adaptation，使用拓扑结构

• UMAD: Universal Model Adaptation under Domain and Category Shift

  • Model adaptation under domain and category shift
  • 在domain和class都有shift的前提下进行模型适配

• A Survey of Unsupervised Domain Adaptation for Visual Recognition

  • A new survey article of domain adaptation
  • 对UDA的一个综述文章，来自作者博士论文

• Unsupervised Domain Adaptation: A Reality Check

  • Doing experiments to show the progress of DA methods over the years
  • 用大量的实验来验证近几年来DA方法的进展

• Hierarchical Optimal Transport for Unsupervised Domain Adaptation

  • hierarchical optimal transport for UDA
  • 层次性的最优传输用于domain adaptation

• Boosting Unsupervised Domain Adaptation with Soft Pseudo-label and Curriculum Learning

  • Using soft pseudo-label and curriculum learning to boost UDA
  • 用软的伪标签和课程学习增强UDA方法

• WACV-22 Semi-supervised Domain Adaptation via Sample-to-Sample Self-Distillation

  • Sample-level self-distillation for semi-supervised DA
  • 样本层次的自蒸馏用于半监督DA

• C-MADA: Unsupervised Cross-Modality Adversarial Domain Adaptation framework for medical Image Segmentation

  • Cross-modality domain adaptation for medical image segmentation
  • 跨模态的DA用于医学图像分割

• Domain Adaptation for Rare Classes Augmented with Synthetic Samples

  • Domain adaptation for rare class
  • 稀疏类的domain adaptation

• BMVC-21 Dynamic Feature Alignment for Semi-supervised Domain Adaptation

  • Dynamic feature alignment for semi-supervised DA
  • 动态特征对齐用于半监督DA

• IEEE TIP-21 Joint Clustering and Discriminative Feature Alignment for Unsupervised Domain Adaptation

  • Clustering and discriminative alignment for DA
  • 聚类与判定式对齐用于DA

• IEEE TNNLS-21 Entropy Minimization Versus Diversity Maximization for Domain Adaptation

  • Entropy minimization versus diversity max for DA
  • 熵最小化与diversity最大化

• Cross-Region Domain Adaptation for Class-level Alignment

  • Cross-region domain adaptation for class-level alignment
  • 跨区域的领域自适应用于类级别的对齐

• EMNLP-21 Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation

  • UDA for machine translation
  • 用领域自适应进行机器翻译

• Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning

  • Domain adaptation for cross-modality liver segmentation
  • 使用domain adaptation进行肝脏的跨模态分割

• CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation

  • Cross-domain transformer for domain adaptation
  • 基于transformer进行domain adaptation

• Robust Ensembling Network for Unsupervised Domain Adaptation

  • Ensembling network for domain adaptation
  • 集成嵌入网络用于domain adaptation

• TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation

  • Vision transformer for domain adaptation
  • 用视觉transformer进行DA

• Learning Transferable Parameters for Unsupervised Domain Adaptation

  • Learning partial transfer parameters for DA
  • 学习适用于迁移部分的参数做UDA任务

• ICCV-21 BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation

  • Bijective MMD for domain adaptation
  • 双射MMD用于语义分割

• MM-21 Few-shot Unsupervised Domain Adaptation with Image-to-class Sparse Similarity Encoding

  • Few-shot DA with image-to-class sparse similarity encoding
  • 小样本的领域自适应

• Dual-Tuning: Joint Prototype Transfer and Structure Regularization for Compatible Feature Learning

  • Prototype transfer and structure regularization
  • 原型的迁移学习

• CVPR-21 Conditional Bures Metric for Domain Adaptation

  • A new metric for domain adaptation
  • 提出一个新的metric用于domain adaptation

• CVPR-21 Generalized Domain Adaptation

  • A general definition for domain adaptation
  • 一个更抽象更一般的domain adaptation定义

• CVPR-21 Reducing Domain Gap by Reducing Style Bias

  • Syle-invariant training for adaptation and generalization
  • 通过训练图像对style无法辨别来进行DA和DG

• 20210706 CVPR-21 Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation

  • Instance affinity learning for domain adaptation
  • 样本间相似度学习，用于DA

• 20210716 BMCV-extend Exploring Dropout Discriminator for Domain Adaptation

  • Using multiple discriminators for domain adaptation
  • 用分布估计代替点估计来做domain adaptation

• 20201208 TIP Effective Label Propagation for Discriminative Semi-Supervised Domain Adaptation

  • 用label propagation做半监督domain adaptation

• 20201203 Unpaired Image-to-Image Translation via Latent Energy Transport

  • 用能量模型做图像翻译

• 20200927 Privacy-preserving Transfer Learning via Secure Maximum Mean Discrepancy

  • 加密情况下的MMD用于迁移学习

• 20200914 A First Step Towards Distribution Invariant Regression Metrics

  • 分布无关的回归评价

• 20200813 ECCV-20 Learning to Cluster under Domain Shift

  • Learning to cluster under domain shift
  • 在domain shift的情况下进行聚类

• 20200706 Learn Faster and Forget Slower via Fast and Stable Task Adaptation

• 20200629 [ICML-20] Graph Optimal Transport for Cross-Domain Alignment

  • Graph OT for cross-domain alignment

• 20191202 AAAI-20 Stable Learning via Sample Reweighting

  • Theoretical sample reweigting
  • 理论和方法，用于sample reweight

• 20191202 arXiv Domain-invariant Stereo Matching Networks

  • Domain-invariant stereo matching networks
  • 领域不变的匹配网络

• 20191202 arXiv Learning Generalizable Representations via Diverse Supervision

  • Diverse supervision helps to learn generalizable representations

• 20191202 arXiv Domain-Aware Dynamic Networks

  • Edge devices adaptative computing
  • 边缘计算上的自适应计算

• 20191029 Reducing Domain Gap via Style-Agnostic Networks

  • Use style-agnostic networks to avoid domain gap
  • 通过风格无关的网络来避免领域的gap

• 20191008 arXiv DIVA: Domain Invariant Variational Autoencoders

  • Domain invariant variational autoencoders
  • 领域不变的变分自编码器

• 20190821 arXiv Transfer Learning-Based Label Proportions Method with Data of Uncertainty

  • Transfer learning with source and target having uncertainty
  • 当source和target都有不确定label时进行迁移

• 20190703 arXiv Inferred successor maps for better transfer learning

  • Inferred successor maps for better transfer learning

• 20190531 IJCAI-19 Adversarial Imitation Learning from Incomplete Demonstrations

  • Adversarial imitation learning from imcomplete demonstrations
  • 基于不完整实例的对抗模仿学习

• 20190517 arXiv Budget-Aware Adapters for Multi-Domain Learning

  • Budget-Aware Adapters for Multi-Domain Learning

• 20190301 SysML-19 FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning

  • An efficient hardware for mobile computer vision applications using transfer learning
  • 提出一个高效的用于移动计算机视觉应用的硬件

• 20190118 arXiv Domain Adaptation for Structured Output via Discriminative Patch Representations

  • Domain adaptation for structured output
  • Domain adaptation用于结构化输出

• 20181217 arXiv When Semi-Supervised Learning Meets Transfer Learning: Training Strategies, Models and Datasets

  • Combining semi-supervised learning and transfer learning
  • 将半监督方法应用于迁移学习

• 20181127 arXiv Privacy-preserving Transfer Learning for Knowledge Sharing

  • First work on privacy preserving in transfer learning
  • 探讨迁移学习中隐私保护的文章

• 20181121 arXiv Not just a matter of semantics: the relationship between visual similarity and semantic similarity

  • Interpreting relationships between visual similarity and semantic similarity
  • 解释了视觉相似性和语义相似性的不同

• 20181008 arXiv Unsupervised Learning via Meta-Learning

  • Meta-learning for unsupervised learning
  • 用于无监督学习的元学习

• 20180919 JMLR Invariant Models for Causal Transfer Learning

  • Invariant models for causal transfer learning
  • 针对causal transfer learning提出不变模型

• 20180912 arXiv Transfer Learning with Neural AutoML

  • Applying transfer learning into autoML search
  • 将迁移学习思想应用于automl

• 20190904 arXiv On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data

  • Train binary classifiers from only unlabeled data
  • 仅从无标记数据训练二分类器

• 20180904 arXiv Learning Data-adaptive Nonparametric Kernels

  • Learn a kernel that can do adaptation
  • 学习一个可以自适应的kernel

• 20180901 arXiv Distance Based Source Domain Selection for Sentiment Classification

  • Propose a new domain selection method by combining existing distance functions
  • 提出一种混合已有多种距离公式的源领域选择方法

• 20180901 KBS Transfer subspace learning via low-rank and discriminative reconstruction matrix

  • Transfer subspace learning via low-rank and discriminative reconstruction matrix
  • 通过低秩和重构进行迁移学习

• 20180825 arXiv Transfer Learning for Estimating Causal Effects using Neural Networks

  • Using transfer learning for casual effect learning
  • 用迁移学习进行因果推理

• 20180724 ICPKR-18 Knowledge-based Transfer Learning Explanation

  • Explain transfer learning things with some knowledge-based theory
  • 用一些基于knowledge的方法解释迁移学习

• 20180628 arXiv 提出Office数据集的实验室又放出一个数据集用于close set、open set、以及object detection的迁移学习：Syn2Real: A New Benchmark forSynthetic-to-Real Visual Domain Adaptation

• 20180604 arXiv 在Open set domain adaptation中，用共享和私有部分重建进行问题的解决：Learning Factorized Representations for Open-set Domain Adaptation

• 20210706 CVPR-21 Multi-Target Domain Adaptation With Collaborative Consistency Learning

  • Using collaborative consistency training for multi-target DA
  • 用多个模型做集成一致性训练进行多目标DA

• 20210625 CVPR-21 Generalized Domain Adaptation

  • Generalized domain adaptation
  • 更通用更一般的domain adaptation

• 20210625 CVPR-21 A Fourier-based Framework for Domain Generalization

  • Fourier based domain generalization
  • 基于傅里叶特征的DG

• 20210329 ICLR-21 Tent: Fully Test-Time Adaptation by Entropy Minimization

  • Test time adaptation by entropy minimization
  • 测试时通过熵最小化进行adaptation

• 20210329 Adversarial Branch Architecture Search for Unsupervised Domain Adaptation

  • NAS for domain adaptation
  • 用神经网络结构搜索做领域自适应

• 20210312 Discrepancy-Based Active Learning for Domain Adaptation

  • Discrepancy and active learning for DA
  • 基于主动学习的DA

• 20210312 Unbalanced minibatch Optimal Transport; applications to Domain Adaptation

  • Unbalanced minibatch OT for DA
  • 非均衡的OT用于DA问题

• 20210127 Hierarchical Domain Invariant Variational Auto-Encoding with weak domain supervision

  • 利用VAE和解耦去做domain generalization
  • Using VAE and disentanglement for domain generalization

• 20201214 WWW-20 Domain Adaptation with Category Attention Network for Deep Sentiment Analysis

  • Unify pivots and non-pivots, and provide interpretability for domain adaptation in sentiment analysis
  • 统一pivots和non-pivots，并提供可解释性进行DA情感分析

• 20201208 NIPS-20 Heuristic Domain Adaptation

  • 启发式domain adaptation

• 20200804 ECCV-20 spotlight Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks

  • 将现有的finetune机制进行扩展
  • Extending finetune mechanism
  • 20200804 ACMMM-20 Adversarial Bipartite Graph Learning for Video Domain Adaptation
    • Video domain adaptation
    • 视频的领域自适应
  • 20200804 MICCAI-20 Whole MILC: generalizing learned dynamics across tasks, datasets, and populations
    • Generalizing across tasks, datasets, populations
    • 在任务、数据集、人群之间做泛化

• 20200724 Learning to Match Distributions for Domain Adaptation

  • 自动深度迁移学习
  • Automatic domain adaptation

• 20200529 TNNLS Deep Subdomain Adaptation Network for Image Classification

  • A fine-grained adaptation method with LMMD, which is very simple and effective
  • 一种细粒度自适应的方法，使用LMMD进行对齐，该方法非常简单有效

• 20200420 arXiv One-vs-Rest Network-based Deep Probability Model for Open Set Recognition

  • One-vs-rest deep model for open set recognition
  • 用于开放集的识别的深度网络

• 20200414 ICLR-20 Gradient as features for deep representation learning

  • Gradients as features for deep representation learning on pretrained models
  • 在预训练模型基础上，将梯度作为额外的feature，提高学习表现

• 20200414 ICLR-20 Domain adaptive multi-branch networks

  • A domain adaptation framework using a multi-branch cascade structure
  • 一个用了多层级联、多分支结构的DA框架

• 20200405 CVPR-20 Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations

  • A simple regularization-based adaptation method
  • 一个非常简单的基于能量最小化的adaptation方法

• 20200210 AAAI-20 Bi-Directional Generation for Unsupervised Domain Adaptation

  • Bidirectional GANs for domain adaptation
  • 双向的GAN用来做DA

• 20191202 PR-19 Correlation-aware Adversarial Domain Adaptation and Generalization

  • CORAL and adversarial for adaptation and generalization
  • 基于CORAL和对抗网络的DA和DG

• 20191201 BMVC-19 Domain Adaptation for Object Detection via Style Consistency

  • Use style consistency for domain adaptation
  • 通过结构一致性来进行domain adaptation

• 20191124 AAAI-20 Knowledge Graph Transfer Network for Few-Shot Recognition

  • GNN for semantic transfer for few-shot learning
  • 用GNN进行类别的语义迁移用于few-shot learning

• 20191124 arXiv Improving Unsupervised Domain Adaptation with Variational Information Bottleneck

  • Information bottleneck for unsupervised da
  • 用了信息瓶颈来进行DA

• 20191124 AAAI-20 (AdaFilter: Adaptive Filter Fine-tuning for Deep Transfer Learning)(https://arxiv.org/abs/1911.09659)

  • Adaptively determine which layer to transfer or finetune
  • 自适应地决定迁移哪个层或微调哪个层

• 20191113 arXiv Knowledge Distillation for Incremental Learning in Semantic Segmentation

  • Knowledge distillation for incremental learning in semantic segmentation
  • 在语义分割问题中针对增量学习进行知识蒸馏

• 20191111 NIPS-19 PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation

  • Multi-scale 3D DA network for point cloud representation

• 20191111 CCIA-19 Feature discriminativity estimation in CNNs for transfer learning

  • Feature discriminativity estimation in CNN for TL

• 20191012 ICCV-19 Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation

  • Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation
  • 直接適應：學習非監督域自適應的判別功能

• 20191015 arXiv Deep Kernel Transfer in Gaussian Processes for Few-shot Learning

  • Deep kernel transfer learing in Gaussian process
  • 高斯过程中的深度迁移学习

• 20191008 EMNLP-19 workshop Domain Differential Adaptation for Neural Machine Translation

  • Embrace the difference between domains for adaptation
  • 拥抱domain的不同，并利用这些不同帮助adaptation

• 20191008 BMVC-19 Multi-Weight Partial Domain Adaptation

  • Class and sample weight contribution for partial domain adaptation
  • 同时考虑类别和样本比重用于部分迁移学习

• 20190813 ICCV-19 oral UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation

  • A unified framework for domain adaptation
  • 一个统一的用于domain adaptation的框架

• 20190809 arXiv Multi-Purposing Domain Adaptation Discriminators for Pseudo Labeling Confidence

  • Improve pseudo label confidence using multi-purposing DA
  • 用多目标DA提高伪标签准确率

• 20190809 arXiv Semi-supervised representation learning via dual autoencoders for domain adaptation

  • Semi-supervised learning via autoencoders
  • 半监督autoencoder用于DA

• 20190809 arXiv Mind2Mind : transfer learning for GANs

  • Transfer learning using GANs
  • 用GAN进行迁移学习

• 20190809 arXiv Self-supervised Domain Adaptation for Computer Vision Tasks

  • Self-supervised DA
  • 自监督DA

• 20190809 arXiv Hidden Covariate Shift: A Minimal Assumption For Domain Adaptation

  • Hidden covariate shift
  • 一种新的DA假设

• 20190809 PR-19 Cross-domain Network Representations

  • Cross-domain network representation learning
  • 跨领域网络表达学习

• 20190809 ICCV-19 Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation

  • Adaptive Feature Norm Approach for Unsupervised Domain Adaptation
  • 自适应的特征归一化用于DA

• 20190731 MICCAI-19 Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation

  • Disentangled representations for unsupervised domain adaptation
  • 基于解耦表征的无监督领域自适应

• 20190719 arXiv Agile Domain Adaptation

  • Domain adaptation by considering the difficulty in classification
  • 通过考虑不同样本分离的难度进行domain adaptation

• 20190718 arXiv Measuring the Transferability of Adversarial Examples

  • Measure the transferability of adversarial examples
  • 度量对抗样本的可迁移性

• 20190604 IJCAI-19 DANE: Domain Adaptive Network Embedding

  • Transfered network embeddings for different networks
  • 不同网络表达的迁移

• 20190604 arXiv Learning to Transfer: Unsupervised Meta Domain Translation

  • Unsupervised meta domain translation
  • 无监督领域翻译

• 20190530 arXiv Learning Bregman Divergences

  • Learning Bregman divergence
  • 学习Bregman差异

• 20190530 arXiv Adversarial Domain Adaptation Being Aware of Class Relationships

  • Using class relationship for adversarial domain adaptation
  • 利用类别关系进行对抗的domain adaptaition

• 20190530 arXiv Cross-Domain Transferability of Adversarial Perturbations

  • Cross-Domain Transferability of Adversarial Perturbations

• 20190525 PAMI-19 Learning More Universal Representations for Transfer-Learning

  • Learning more universal representations for transfer learning
  • 对迁移学习设计2种方式学到更通用的表达

• 20190517 ICML-19 Learning What and Where to Transfer

  • Learning what and where to transfer in deep networks
  • 学习深度网络从何处迁移

• 20190517 ICML-19 Zero-Shot Voice Style Transfer with Only Autoencoder Loss

  • Zero-shot voice style transfer with only autoencoder loss
  • 零次声音迁移

• 20190515 CVPR-19 Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection

  • Domain adaptation for object detection
  • 领域自适应用于物体检测

• 20190507 NAACL-HLT 19 Transfer of Adversarial Robustness Between Perturbation Types

  • Transfer of Adversarial Robustness Between Perturbation Types

• 20190416 arXiv ACE: Adapting to Changing Environments for Semantic Segmentation

  • Propose a new method that can adapt to new environments
  • 提出一种可以适配不同环境的方法

• 20190416 arXiv Incremental multi-domain learning with network latent tensor factorization

  • Incremental multi-domain learning with network latent tensor factorization
  • 网络隐性tensor分解应用于多领域增量学习

• 20190415 PAKDD-19 Parameter Transfer Unit for Deep Neural Networks

  • Propose a parameter transfer unit for DNN
  • 对深度网络提出参数迁移单元

• 20190412 PAMI-19 Beyond Sharing Weights for Deep Domain Adaptation

  • Domain adaptation by not sharing weights
  • 通过不共享权重来进行domain adaptation

• 20190405 IJCNN-19 Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning

  • The first work to accelerate transfer learning
  • 第一个加速迁移学习的工作

• 20190102 WSDM-19 Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching

  • Reinforced transfer learning for deep text matching
  • 迁移学习进行深度文本匹配

• 20190102 arXiv DART: Domain-Adversarial Residual-Transfer Networks for Unsupervised Cross-Domain Image Classification

  • Adversarial + residual for domain adaptation
  • 对抗+残差进行domain adaptation

• 20181220 arXiv TWINs: Two Weighted Inconsistency-reduced Networks for Partial Domain Adaptation

  • Two weighted inconsistency-reduced networks for partial domain adaptation
  • 两个权重网络用于部分domain adaptation

• 20181127 arXiv Learning Grouped Convolution for Efficient Domain Adaptation

  • Group convolution for domain adaptation
  • 群体卷积进行domain adaptation

• 20181121 arXiv Unsupervised Domain Adaptation: An Adaptive Feature Norm Approach

  • A nonparametric method for domain adaptation
  • 一种无参数的domain adaptation方法

• 20181121 arXiv Domain Adaptive Transfer Learning with Specialist Models

  • Sample reweighting methods for domain adaptative
  • 样本权重更新法进行domain adaptation

• 20180926 ICLR-18 Self-ensembling for visual domain adaptation

  • Self-ensembling for domain adaptation
  • 将self-ensembling应用于da

• 20180620 CVPR-18 用迁移学习进行fine tune：Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning

• 20180321 CVPR-18 构建了一个迁移学习算法，用于解决跨数据集之间的person-reidenfication: Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatial-Temporal Patterns

• 20180315 ICLR-17 一篇综合进行two-sample stest的文章：Revisiting Classifier Two-Sample Tests

• 20171214 arXiv Investigating the Impact of Data Volume and Domain Similarity on Transfer Learning Applications

  • 在实验中探索了数据量多少，和相似度这两个因素对迁移学习效果的影响

• NIPS-17 Learning Multiple Tasks with Multilinear Relationship Networks

• 20210420 arXiv On Universal Black-Box Domain Adaptation

  • Universal black-box domain adaptation
  • 黑盒情况下的universal domain adaptation

• 20210319 Learning Invariant Representations across Domains and Tasks

  • Automatically learn to match distributions
  • 自动适配分布的任务适配网络

• 20191222 arXiv Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion

  • Generate data without priors for transfer learning based on deep dream
  • 只用网络架构不用原来数据，生成新数据用于迁移

• 20191201 arXiv A Unified Framework for Lifelong Learning in Deep Neural Networks

  • A unified framework for life-long learing in DNN

• 20191201 arXiv ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring

  • Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring

• 20191119 NIPS-19 workshop Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis

  • Ensemble DA using noise labels
  • 在ensemble中出现noise label时如何处理

• 20191029 KBS Semi-supervised representation learning via dual autoencoders for domain adaptation

  • Semi-supervised domain adaptation with autoencoders
  • 用自动编码器进行半监督的DA

• 20190926 arXiv Learning a Domain-Invariant Embedding for Unsupervised Domain Adaptation Using Class-Conditioned Distribution Alignment

  • Use class-conditional DA for domain adaptation
  • 使用类条件对齐进行domain adaptation

• 20190926 arXiv A Deep Learning-Based Approach for Measuring the Domain Similarity of Persian Texts

  • Deep learning based domain similarity learning
  • 利用深度学习进行领域相似度的学习

• 20190926 arXiv FEED: Feature-level Ensemble for Knowledge Distillation

  • Feature-level knowledge distillation
  • 特征层面的知识蒸馏

• 20190926 ICCV-19 Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identification

  • A simple approach for domain adaptation
  • 一个很简单的DA方法

• 20190910 BMVC-19 Curriculum based Dropout Discriminator for Domain Adaptation

  • Curriculum dropout for domain adaptation
  • 基于课程学习的dropout用于DA

• 20190909 PAMI Inferring Latent Domains for Unsupervised Deep Domain Adaptation

  • Inferring latent domains for unsupervised deep domain
  • 在深度迁移学习中推断隐含领域

• 20190729 ICCV workshop Multi-level Domain Adaptive learning for Cross-Domain Detection

  • Multi-level domain adaptation for cross-domain Detection
  • 多层次的domain adaptation

• 20190626 IJCAI-19 Bayesian Uncertainty Matching for Unsupervised Domain Adaptation

  • Bayesian uncertainty matching for da
  • 贝叶斯网络用于da

• 20190419 CVPR-19 DDLSTM: Dual-Domain LSTM for Cross-Dataset Action Recognition

  • Dual-Domain LSTM for Cross-Dataset Action Recognition
  • 跨数据集的动作识别

• 20190109 InfSc Robust Unsupervised Domain Adaptation for Neural Networks via Moment Alignment

  • Extension of Central Moment Discrepancy (ICLR-17) approach

• 20181212 ICONIP-18 Domain Adaptation via Identical Distribution Across Models and Tasks

  • Transfer from large net to small net
  • 从大网络迁移到小网络

• 20181212 AIKP Deep Domain Adaptation

  • Low-rank + deep nn for domain adaptation
  • Low-rank用于深度迁移

• 20181211 arXiv Deep Variational Transfer: Transfer Learning through Semi-supervised Deep Generative Models

  • Transfer learning with deep generative model
  • 通过深度生成模型进行迁移学习

• 20181121 arXiv Integrating domain knowledge: using hierarchies to improve deep classifiers

  • Using hierarchies to help deep learning
  • 借助于层次关系来帮助深度网络进行学习

• 20181117 arXiv AdapterNet - learning input transformation for domain adaptation

  • Learning input transformation for domain adaptation
  • 对domain adaptation任务学习输入的自适应

• 20181115 AAAI-19 Exploiting Local Feature Patterns for Unsupervised Domain Adaptation

  • Local domain alignment for domain adaptation
  • 局部领域自适应

• 20181115 NIPS-18 Co-regularized Alignment for Unsupervised Domain Adaptation

  • The idea is the same with the above one...

• 20181113 NIPS-18 Generalized Zero-Shot Learning with Deep Calibration Network

  • Deep calibration network for zero-shot learning
  • 提出deep calibration network进行zero-shot learning

• 20181110 AAAI-19 Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons

  • Transfer learning for bounding neuron activation boundaries
  • 使用迁移学习进行神经元激活边界判定

• 20181108 arXiv Deep feature transfer between localization and segmentation tasks

  • Feature transfer between localization and segmentation
  • 在定位与分割任务间进行迁移

• 20181107 BigData-18 Transfer learning for time series classification

  • First work on deep transfer learning for time series classification
  • 第一个将深度迁移学习用于时间序列分类

• 20181106 PRCV-18 Deep Local Descriptors with Domain Adaptation

  • Adding MMD layers to conv and fc layers
  • 在卷积和全连接层都加入MMD

• 20181106 LNCS-18 LSTN: Latent Subspace Transfer Network for Unsupervised Domain Adaptation

  • Combine subspace learning and neural network for DA
  • 将子空间表示和深度网络结合起来用于DA

• 20181105 SIGGRAPI-18 Unsupervised representation learning using convolutional and stacked auto-encoders: a domain and cross-domain feature space analysis

  • Representation learning for cross-domains
  • 跨领域的特征学习

• 20181105 arXiv Progressive Memory Banks for Incremental Domain Adaptation

  • Progressive memory bank in RNN for incremental DA
  • 针对增量的domain adaptation，进行记忆单元的RNN学习

• 20180901 arXiv Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation

  • deep domain adaptation + intra-class / inter-class distance
  • 深度domain adaptation再加上类内类间距离学习

• 20180819 arXiv Conceptual Domain Adaptation Using Deep Learning

  • A search framework for deep transfer learning
  • 提出一个可以搜索的framework进行迁移学习

• 20180731 ECCV-18 DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation

  • Deep + Joint distribution adaptation + optimal transport
  • 深度 + 联合分布适配 + optimal transport

• 20180731 ICLR-18 Few Shot Learning with Simplex

  • Represent deep learning using the simplex
  • 用单纯性表征深度学习

• 20180724 AIAI-18 Improving Deep Models of Person Re-identification for Cross-Dataset Usage

  • apply deep models to cross-dataset RE-ID
  • 将深度迁移学习应用于跨数据集的Re-ID

• 20180724 ECCV-18 Zero-Shot Deep Domain Adaptation

  • Perform zero-shot domain adaptation when there is no target domain data available
  • 当目标领域的数据不可用时如何进行domain adaptation :

• 20180724 ICCSE-18 Deep Transfer Learning for Cross-domain Activity Recognition

  • Provide source domain selection and activity recognition for cross-domain activity recognition
  • 提出了跨领域行为识别中的深度方法模型，以及相关的领域选择方法

• 20180530 arXiv 用于深度网络的鲁棒性domain adaptation方法：Robust Unsupervised Domain Adaptation for Neural Networks via Moment Alignment

• 20180522 arXiv 用CNN进行跨领域的属性学习：Cross-domain attribute representation based on convolutional neural network

• 20180428 CVPR-18 相互协同学习：Deep Mutual Learning

• 20180428 ICLR-18 自集成学习用于domain adaptation：Self-ensembling for visual domain adaptation

• 20180428 IJCAI-18 将knowledge distilation用于transfer learning，然后进行视频分类：Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph Distillation for Video Classification

• 20180426 arXiv 深度学习中的参数如何进行迁移？（杨强团队）：Parameter Transfer Unit for Deep Neural Networks

• 20180425 CVPR-18(oral) 对不同的视觉任务进行建模，从而可以进行深层次的transfer：Taskonomy: Disentangling Task Transfer Learning

• 20180410 ICLR-17 第一篇用可变RNN进行多维时间序列迁移的文章：Variational Recurrent Adversarial Deep Domain Adaptation

• 20180403 arXiv 本地和云端CNN迁移融合的图片分类：Hierarchical Transfer Convolutional Neural Networks for Image Classification

• 20180402 CVPR-18 渐进式domain adaptation：Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation

• 20180329 arXiv 基于attention机制的多任务学习：End-to-End Multi-Task Learning with Attention

• 20180326 arXiv 将迁移学习用于Faster R-CNN对象识别中：Domain Adaptive Faster R-CNN for Object Detection in the Wild

• 20180326 Pattern Recognition-17 多标签迁移学习方法应用于脸部属性分类：Multi-label Learning Based Deep Transfer Neural Network for Facial Attribute Classification

• 20180326 类似于ResNet的思想，在传统layer的ReLU之前加一个additive layer进行domain adaptation，思想简洁，效果非常好：Layer-wise domain correction for unsupervised domain adaptation

• 20180326 Pattern Recognition-17 基于Batch normalization提出了AdaBN，很简单：Adaptive Batch Normalization for practical domain adaptation

• 20180309 arXiv 利用已有网络的先验知识来加速目标网络的训练：Transfer Automatic Machine Learning

• 2018 ICLR-18 最小熵领域对齐方法 Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation code

• ICLR-17 Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning

• ICCV-17 AutoDIAL: Automatic DomaIn Alignment Layers

• ICCV-17 CCSA: Unified Deep Supervised Domain Adaptation and Generalization

• ICML-17 JAN: Deep Transfer Learning with Joint Adaptation Networks

• 2017 Google: Learning Transferable Architectures for Scalable Image Recognition

• NIPS-16 RTN: Unsupervised Domain Adaptation with Residual Transfer Networks

• CoRR abs/1603.04779 (2016) AdaBN: Revisiting batch normalization for practical domain adaptation

• JMLR-16 DANN: Domain-adversarial training of neural networks

• 20171226 NIPS 2016 把传统工作搬到深度网络中的范例：不是只学习domain之间的共同feature，还学习每个domain specific的feature。这篇文章写得非常清楚，通俗易懂！ Domain Separation Networks | 代码

• 20171222 ICCV 2017 对于target中只有很少量的标记数据，用深度网络结合孪生网络的思想进行泛化：Unified Deep Supervised Domain Adaptation and Generalization | 代码和数据

• 20171126 NIPS-17 Label Efficient Learning of Transferable Representations acrosss Domains and Tasks

  • 李飞飞小组发在NIPS 2017的文章。针对不同的domain、不同的feature、不同的label space，统一学习一个深度网络进行表征。

• 201711 一个很好的深度学习+迁移学习的实践教程，有代码有数据，可以直接上手：基于深度学习和迁移学习的识花实践

• ECCV-16 Deep CORAL: Correlation Alignment for Deep Domain Adaptation

• ECCV-16 DRCN: Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation

• ICML-15 DAN: Learning Transferable Features with Deep Adaptation Networks

• ICML-15 GRL: Unsupervised Domain Adaptation by Backpropagation

• ICCV-15 Simultaneous Deep Transfer Across Domains and Tasks

• NIPS-14 How transferable are features in deep neural networks?

• CoRR abs/1412.3474 (2014) Deep Domain Confusion(DDC): Maximizing for Domain Invariance

• 深度联合适配网络（Joint Adaptation Network, JAN）

  • Deep Transfer Learning with Joint Adaptation Networks
  • 延续了之前的DAN工作，这次考虑联合适配

• 20191214 arXiv Learning Domain Adaptive Features with Unlabeled Domain Bridges

  • Learning domain adaptive features with unlabeled CycleGAN

• 20191214 AAAI-20 Adversarial Domain Adaptation with Domain Mixup

  • Domain adaptation with data mixup

• 20190916 arXiv Compound Domain Adaptation in an Open World

  • Domain adaptation using the target domain knowledge
  • 使用目标域的知识来进行domain adaptation

• 20101008 ICCV-19 Enhancing Adversarial Example Transferability with an Intermediate Level Attack

  • Enhancing adversarial examples transerability
  • 增强对抗样本的可迁移性

• 20190408 arXiv DeceptionNet: Network-Driven Domain Randomization

  • Using only source data for domain randomization
  • 仅利用源域数据进行domain randomization

• 20190220 arXiv Unsupervised Domain Adaptation using Deep Networks with Cross-Grafted Stacks

  • Domain adaptation using deep learning with cross-grafted stacks
  • 用跨领域嫁接栈进行domain adaptation

• 20181217 arXiv DLOW: Domain Flow for Adaptation and Generalization

  • Domain flow for adaptation and generalization
  • 域流方法应用于领域自适应和扩展

• 20181212 arXiv Learning Transferable Adversarial Examples via Ghost Networks

  • Use ghost networks to learn transferrable adversarial examples
  • 使用ghost网络来学习可迁移的对抗样本

• 20181205 arXiv Unsupervised Domain Adaptation using Generative Models and Self-ensembling

  • UDA using CycleGAN
  • 基于CycleGAN的domain adaptation

• 20181205 arXiv VADRA: Visual Adversarial Domain Randomization and Augmentation

  • Domain randomization and augmentation
  • Domain randomization和增强

• 20181128 arXiv Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping

  • CycleGAN for domain adaptation

• 20181127 arXiv Distorting Neural Representations to Generate Highly Transferable Adversarial Examples

  • Generate transferrable examples to fool networks
  • 生成一些可迁移的对抗样本来迷惑神经网络，在各个网络上都表现好

• 20181123 arXiv Progressive Feature Alignment for Unsupervised Domain Adaptation

  • Progressively selecting confident pseudo labeled samples for transfer
  • 渐进式选择置信度高的伪标记进行迁移

• 20181113 NIPS-18 Conditional Adversarial Domain Adaptation

  • Using conditional GAN for domain adaptation
  • 用conditional GAN进行domain adaptation

• 20181107 NIPS-18 Invariant Representations without Adversarial Training

  • Get invariant representations without adversarial training
  • 不进行对抗训练获得不变特征表达

• 20181105 arXiv Efficient Multi-Domain Dictionary Learning with GANs

  • Dictionary learning for multi-domains using GAN
  • 用GAN进行多个domain的字典学习

• 20181012 arXiv Domain Confusion with Self Ensembling for Unsupervised Adaptation

  • Domain confusion and self-ensembling for DA
  • 用于Domain adaptation的confusion和self-ensembling方法

• 20180912 arXiv Improving Adversarial Discriminative Domain Adaptation

  • Improve ADDA using source domain labels
  • 提高ADDA方法的精度，使用source domain的label

• 20180731 ECCV-18 Dist-GAN: An Improved GAN using Distance Constraints

  • Embed an autoencoder in GAN to improve its stability in training and propose two distances
  • 将autoencoder集成到GAN中，提出相应的两种距离进行度量，提高了GAN的稳定性

• 20180724 ECCV-18 Unsupervised Image-to-Image Translation with Stacked Cycle-Consistent Adversarial Networks

  • Using stacked CycleGAN to perform image-to-image translation
  • 用stacked cycleGAN进行image-to-image的翻译

• 20180628 ICML-18 Pixel-level和feature-level的domain adaptation：CyCADA: Cycle-Consistent Adversarial Domain Adaptation

• 20180619 CVPR-18 将optimal transport加入adversarial中进行domain adaptation：Re-weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation

• 20180616 CVPR-18 用GAN进行domain adaptation：Generate To Adapt: Aligning Domains using Generative Adversarial Networks

• 20180612 ICML-18 利用多个数据集辅助，从而提高目标领域的学习能力：RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks

• 20180612 ICML-18 利用GAN进行多个domain的联合分布优化：JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets

• 20180605 arXiv NAM: Non-Adversarial Unsupervised Domain Mapping

• 20180508 arXiv 利用GAN，从有限数据中生成另一个domain的数据：Transferring GANs: generating images from limited data

• 20180501 arXiv Open set domain adaptation的对抗网络版本：Open Set Domain Adaptation by Backpropagation

• 20180427 arXiv 提出了adversarial residual的概念，进行深度对抗迁移：Unsupervised Domain Adaptation with Adversarial Residual Transform Networks

• 20180424 CVPR-18 用GAN和迁移学习进行图像增强：Adversarial Feature Augmentation for Unsupervised Domain Adaptation

• 20180413 arXiv 一种思想非常简单的深度迁移方法，仅考虑进行domain之间的类别概率矫正就能取得非常好的效果：Simple Domain Adaptation with Class Prediction Uncertainty Alignment

• 20180413 arXiv Mingming Gong提出的用因果生成网络进行深度迁移：Causal Generative Domain Adaptation Networks

• 20180410 CVPR-18(oral) 用两个分类器进行对抗迁移：Maximum Classifier Discrepancy for Unsupervised Domain Adaptation 代码

• 20180403 CVPR-18 将样本权重应用于对抗partial transfer中：[Importance Weighted Adversarial Nets for Partial Domain Adaptation](https://arxiv.org/abs/1803.09210

• 20180326 MLSP-17 把domain separation network和对抗结合起来，提出了一个对抗网络进行迁移学习：Adversarial domain separation and adaptation

• 20180326 ICIP-17 类似于domain separation network，加入了对抗判别训练，同时优化分类、判别、相似度三个loss：Semi-supervised domain adaptation via convolutional neural network

• 20180116 ICLR-18 用对偶的形式替代对抗训练中原始问题的表达，从而进行分布对齐 Stable Distribution Alignment using the Dual of the Adversarial Distance

• 20180111 arXiv 在GAN中用原始问题的对偶问题替换max问题，使得梯度更好收敛 Stable Distribution Alignment Using the Dual of the Adversarial Distance

• 20180110 AAAI-18 将Wasserstein GAN用到domain adaptaiton中 Wasserstein Distance Guided Representation Learning for Domain Adaptation

• 201707 CVPR-17 Adversarial Representation Learning For Domain Adaptation

• AAAI-18 Multi-Adversarial Domain Adaptation

• ICCV-17 CycleGAN: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

• ICCV-17 DualGAN: DualGAN: Unsupervised Dual Learning for Image-to-Image Translation

• CVPR-17 Asymmetric Tri-training for Unsupervised Domain Adaptation

• ICML-17 DiscoGAN: Learning to Discover Cross-Domain Relations with Generative Adversarial Networks

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Domain generalization

Survey

• TKDE-22 Generalizing to Unseen Domains: A Survey on Domain Generalization | 知乎文章 | 微信公众号 | Code
  • First survey on domain generalization
  • 第一篇对Domain generalization (领域泛化)的综述

Tutorial

• WSDM-23 and IJCAI-22 A tutorial on domain generalization [link] | [website]
  • A tutorial on domain generalization

Papers

• Beyond Empirical Risk Minimization: Local Structure Preserving Regularization for Improving Adversarial Robustness [arxiv]

  • Local structure preserving for adversarial robustness 通过保留局部结构来进行对抗鲁棒性

• TFS-ViT: Token-Level Feature Stylization for Domain Generalization [arxiv]

  • Token-level feature stylization for domain generalization 用token-level特征变换进行domain generalization

• Are Data-driven Explanations Robust against Out-of-distribution Data? [arxiv]

  • Data-driven explanations robust? 探索数据驱动的解释是否是OOD鲁棒的

• ERM++: An Improved Baseline for Domain Generalization [arxiv]

  • Improved ERM for domain generalization 提高的ERM用于domain generalization

• Complementary Domain Adaptation and Generalization for Unsupervised Continual Domain Shift Learning [arxiv]

  • Continual domain shift learning using adaptation and generalization 使用 adaptation和DG进行持续分布变化的学习

• CVPR'23 TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization [arxiv]

  • Improve generalization and adversarial robustness 同时提高鲁棒性和泛化性

• Finding Competence Regions in Domain Generalization [arxiv]

  • Finding competence regions in domain generalization 在DG中发现能力区域

• CVPR'23 ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain Generalization [arxiv]

  • A lightweight module for domain generalization 一个用于DG的轻量级模块

• CVPR'23 Sharpness-Aware Gradient Matching for Domain Generalization [arxiv]

  • Sharpness-aware gradient matching for DG 利用梯度匹配进行domain generalization

• Domain Generalization via Nuclear Norm Regularization [arxiv]

  • Domain generalization via nuclear norm regularization 使用核归一化进行domain generalization

• Imbalanced Domain Generalization for Robust Single Cell Classification in Hematological Cytomorphology [arxiv]

  • Imbalanced domain generalization for single cell classification 不平衡的DG用于单细胞分类

• FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learning [arxiv]

  • Fast generalization for federated CLIP 在联邦中进行快速的CLIP训练

• Robust Representation Learning with Self-Distillation for Domain Generalization [arxiv]

  • Robust representation learning with self-distillation

• ICLR-23 Temporal Coherent Test-Time Optimization for Robust Video Classification [arxiv]

  • Temporal distribution shift in video classification

• On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective [arxiv] | [code]

  • Adversarial and OOD evaluation of ChatGPT 对ChatGPT鲁棒性的评测

• How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts? [arxiv]

  • Regression models uncertainty for distribution shift 回归模型对于分布漂移的不确定性

• ICLR'23 SoftMatch: Addressing the Quantity-Quality Tradeoff in Semi-supervised Learning [arxiv]

  • Semi-supervised learning algorithm 解决标签质量问题的半监督学习方法

• Empirical Study on Optimizer Selection for Out-of-Distribution Generalization [arxiv]

  • Opimizer selection for OOD generalization OOD泛化中的学习器选择

• ICML'22 Understanding the failure modes of out-of-distribution generalization [arxiv]

  • Understand the failure modes of OOD generalization 探索OOD泛化中的失败现象

• ICLR'23 Out-of-distribution Representation Learning for Time Series Classification [arxiv]

  • OOD for time series classification 时间序列分类的OOD算法

• CLIP the Gap: A Single Domain Generalization Approach for Object Detection [arxiv]

  • Using CLIP for domain generalization object detection 使用CLIP进行域泛化的目标检测

• TMLR'22 A Unified Survey on Anomaly, Novelty, Open-Set, and Out of-Distribution Detection: Solutions and Future Challenges [openreview]

  • A recent survey on OOD/anomaly detection 一篇最新的关于OOD/anomaly detection的综述

• NeurIPS'18 A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks [paper]

  • Using class-conditional distribution for OOD detection 使用类条件概率进行OOD检测

• ICLR'22 Discrete Representations Strengthen Vision Transformer Robustness [arxiv]

  • Embed discrete representation for OOD generalization 在ViT中加入离散表征增强OOD性能

• Learning to Learn Domain-invariant Parameters for Domain Generalization [[arxiv](Learning to Learn Domain-invariant Parameters for Domain Generalization)]

  • Learning to learn domain-invariant parameters for DG 元学习进行domain generalization

• HMOE: Hypernetwork-based Mixture of Experts for Domain Generalization [arxiv]

  • Hypernetwork-based ensembling for domain generalization 超网络构成的集成学习用于domain generalization

• The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning [arxiv]

  • OOD using fine-tuning 系统总结了基于fine-tuning进行OOD的一些结果

• GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective [arxiv]

  • OOD for natural language processing evaluation 提出GLUE-X用于OOD在NLP数据上的评估

• CVPR'22 Delving Deep Into the Generalization of Vision Transformers Under Distribution Shifts [arxiv]

  • Vision transformers generalization under distribution shifts 评估ViT的分布漂移

• NeurIPS'22 Models Out of Line: A Fourier Lens on Distribution Shift Robustness [arxiv]

  • A fourier lens on distribution shift robustness 通过傅里叶视角来看分布漂移的鲁棒性

• Normalization Perturbation: A Simple Domain Generalization Method for Real-World Domain Shifts [arxiv]

  • Normalization perturbation for domain generalization 通过归一化扰动来进行domain generalization

• FIXED: Frustraitingly easy domain generalization using Mixup [arxiv]

  • 使用Mixup进行domain generalization

• Learning to Learn Domain-invariant Parameters for Domain Generalization [arxiv]

  • Learning to learn domain-invariant parameters for domain generalization

• NeurIPS'22 LOG: Active Model Adaptation for Label-Efficient OOD Generalization [openreview]

  • Model adaptation for label-efficient OOD generalization

• NeurIPS'22 Domain Generalization without Excess Empirical Risk [openreview]

  • Domain generalization without excess empirical risk

• NeurIPS'22 FedSR: A Simple and Effective Domain Generalization Method for Federated Learning [openreview]

  • FedSR for federated learning domain generalization 用于联邦学习的domain generalization

• NeurIPS'22 Probable Domain Generalization via Quantile Risk Minimization [openreview]

  • Domain generalization with quantile risk minimization 用quantile风险最小化的domain generalization

• NeurIPS'22 Your Out-of-Distribution Detection Method is Not Robust! [openreview]

  • OOD models are not robust 分布外泛化模型不够鲁棒

• PhDthesis Generalizing in the Real World with Representation Learning [arxiv]

  • A phd thesis about generalization in real world 一篇关于现实世界如何做Generalization的博士论文

• The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning [arxiv]

  • Evolution of OOD robustness by fine-tuning

• Out-of-Distribution Generalization in Algorithmic Reasoning Through Curriculum Learning [arxiv]

  • OOD in algorithmic reasoning 算法reasoning过程中的OOD

• Towards Out-of-Distribution Adversarial Robustness [arxiv]

  • OOD adversarial robustness OOD对抗鲁棒性

• TripleE: Easy Domain Generalization via Episodic Replay [arxiv]

  • Easy domain generalization by episodic replay

• Deep Spatial Domain Generalization [arxiv]

  • Deep spatial domain generalization

• Assaying Out-Of-Distribution Generalization in Transfer Learning [arXiv]

  • A lot of experiments to show OOD performance

• ICML-21 Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization [arxiv]

  • Strong correlation between ID and OOD

• Generalized representations learning for time series classification[arxiv]

  • OOD for time series classification 域泛化用于时间序列分类

• Language-aware Domain Generalization Network for Cross-Scene Hyperspectral Image Classification [arxiv]

  • Domain generalization for cross-scene hyperspectral image classification 域泛化用于高光谱图像分类

• Improving Robustness to Out-of-Distribution Data by Frequency-based Augmentation arxiv

  • OOD by frequency-based augmentation 通过基于频率的数据增强进行OOD

• Domain Generalization for Prostate Segmentation in Transrectal Ultrasound Images: A Multi-center Study arxiv

  • Domain generalizationfor prostate segmentation 领域泛化用于前列腺分割

• Domain Adaptation from Scratch arxiv

  • Domain adaptation from scratch

• Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution arxiv

  • Model selection for domain generalization 域泛化中的模型选择问题

• Equivariant Disentangled Transformation for Domain Generalization under Combination Shift

  • Equivariant disentangled transformation for domain generalization 新的建模domain generalization的思路

• ECCV-22 workshop Domain-Specific Risk Minimization

  • Domain-specific risk minization for OOD 领域特异性风险最小化用于域泛化

• IJCAI-22 Domain Generalization through the Lens of Angular Invariance

  • Using angular invariance for domain generalization 使用角度不变性进行domain generalization

• Adaptive Domain Generalization via Online Disagreement Minimization

  • Online domain generalization via disagreement minimization 在线DG

• Self-Distilled Vision Transformer for Domain Generalization

  • Vision transformer for domain generalization 用ViT做domain generalization

• TMLR-22 Domain-invariant Feature Exploration for Domain Generalization

  • Exploring domain-invariant feature for domain generalization 探索领域不变特征在领域泛化中的应用

• TIST-22 Domain Generalization for Activity Recognition via Adaptive Feature Fusion

  • Domain generalization for activity recognition 领域泛化用于行为识别

• The Importance of Background Information for Out of Distribution Generalization

  • Background information for OOD generalization 背景信息对于OOD泛化的重要性

• Causal Balancing for Domain Generalization

  • Causal balancing for domain generalization 因果平衡用于领域泛化

• Temporal Domain Generalization with Drift-Aware Dynamic Neural Network

  • Temporal domain generalization with drift-aware dynamic neural network 时序域泛化

• Multiple Domain Causal Networks

  • Mlutiple domain causal networks 多领域的因果网络

• IJCAI-21 Test-time Fourier Style Calibration for Domain Generalization

  • Test-time calibration for domain generalization 用傅立叶变化进行域泛化的测试时矫正

• Out-Of-Distribution Detection In Unsupervised Continual Learning

  • OOD detection in unsupervised continual learning 无监督持续学习中进行OOD检测

• ICLR-22 Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution

  • Fin-tuning and linear probing for ood generalization
  • 先linear probing最后一层再finetune对OOD任务最好

• ICLR-22 Asymmetry Learning for Counterfactually-invariant Classification in OOD Tasks

  • Asymmetry learning for OOD tasks
  • 非对称学习用于OOD任务

• Improving Generalization in Federated Learning by Seeking Flat Minima

  • Seeking flat minima for domain generalization in federated learning
  • 通过寻找平坦值进行联邦学习领域泛化

• Gated Domain-Invariant Feature Disentanglement for Domain Generalizable Object Detection

  • Channel masking for domain generalization object detection
  • 通过一个gate控制channel masking进行object detection DG

• A Broad Study of Pre-training for Domain Generalization and Adaptation

  • A broad study of pre-training models for DA and DG
  • 大量的实验进行DA和DG

• Learning Semantic Segmentation from Multiple Datasets with Label Shifts

  • Learning semantic segmentation from many datasets with label shifts
  • 在有标签漂移的情况下从多个数据集中学习语义分割

• PAKDD-22 Layer Adaptive Deep Neural Networks for Out-of-distribution Detection

  • Layer adaptive network for OOD detection
  • 层自适应的网络进行OOD检测

• ICLR-22 oral A Fine-Grained Analysis on Distribution Shift

  • Extensive experiments on distribution shift for OOD
  • 大量的实验进行OOD验证

• ICLR-22 oral Fine-Tuning Distorts Pretrained Features and Underperforms Out-of-Distribution

  • Fine-tuning with linear probing for OOD
  • 微调加上linear probing用于OOD

• ICLR-22 Uncertainty Modeling for Out-of-Distribution Generalization

  • Uncertainty modeling for OOD generalization
  • 用于分布外泛化的不确定性建模

• TKDE-22 Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection

  • Adaptiev memory network for anomaly detection
  • 自适应的记忆网络用于异常检测

• ICIP-22 Meta-Learned Feature Critics for Domain Generalized Semantic Segmentation

  • Meta-learning for domain generalization
  • 元学习用于domain generalization

• ICIP-22 Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains

  • Few-shot generalization using meta-learning
  • 用元学习进行小样本的泛化

• More is Better: A Novel Multi-view Framework for Domain Generalization

  • Multi-view learning for domain generalization
  • 使用多视图学习来进行domain generalization

• Unsupervised Domain Generalization by Learning a Bridge Across Domains

  • Unsupervised domain generalization
  • 无监督的domain generalization

• ROBIN : A Benchmark for Robustness to Individual Nuisancesin Real-World Out-of-Distribution Shifts

  • A benchmark for robustness to individual OOD
  • 一个OOD的benchmark

• ICML-21 workshop Towards Principled Disentanglement for Domain Generalization

  • Principled disentanglement for domain generalization
  • Principled解耦用于domain generalization

• Federated Learning with Domain Generalization

  • Federated domain generalization

  • 联邦学习+domain generalization

  • Semi-Supervised Domain Generalization in Real World:New Benchmark and Strong Baseline

    • Semi-supervised domain generalization
    • 半监督+domain generalization

  • MICCAI-21 Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning

    • Domain generalization for mammography detection
    • 领域泛化用于乳房X射线检查

• WACV-21 Domain Generalization through Audio-Visual Relative Norm Alignment in First Person Action Recognition

  • Domain generalization by audio-visual alignment
  • 通过音频-视频对齐进行domain generalization

• Dynamically Decoding Source Domain Knowledge For Unseen Domain Generalization

  • Ensemble learning for domain generalization
  • 用集成学习进行domain generalization

• Scale Invariant Domain Generalization Image Recapture Detection

  • Scale invariant domain generalizaiton
  • 尺度不变的domain generalization

• ICCV-21 Shape-Biased Domain Generalization via Shock Graph Embeddings

  • Domain generalization based on shape information
  • 基于形状进行domain generalization

• Domain and Content Adaptive Convolution for Domain Generalization in Medical Image Segmentation

  • Domain generalization for medical image segmentation
  • 领域泛化用于医学图像分割

• Fishr: Invariant Gradient Variances for Out-of-distribution Generalization

  • Invariant gradient variances for OOD generalization
  • 不变梯度方差，用于OOD

• Class-conditioned Domain Generalization via Wasserstein Distributional Robust Optimization

  • Domain generalization with wasserstein DRO
  • 使用Wasserstein DRO进行domain generalization

• CIKM-21 AdaRNN: Adaptive Learning and Forecasting of Time Series Code 知乎文章 Video

  • A new perspective to using transfer learning for time series analysis
  • 一种新的建模时间序列的迁移学习视角

• 20190531 arXiv Image Alignment in Unseen Domains via Domain Deep Generalization

  • Deep domain generalization for image alignment
  • 深度领域泛化用于图像对齐

• 20200821 ECCV-20 Towards Recognizing Unseen Categories in Unseen Domains

  • Recognizing unseen classes in unseen domains
  • 对未知领域识别未知类

• 20200706 ICLR-21 In Search of Lost Domain Generalization

• 20201016 Energy-based Out-of-distribution Detection

  • Energy-based OOD

• 20201222 AAAI-21 DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation

  • OOD generalization
  • 用特征分解和语义增强做OOD泛化

• 20210106 Style Normalization and Restitution for Domain Generalization and Adaptation

  • Style normalization and restitution for DA and DG
  • 风格归一化用于DA和DG任务

• CVPR-21 Uncertainty-Guided Model Generalization to Unseen Domains

  • Uncertainty-guided generalization
  • 基于不确定性的domain generalization

• CVPR-21 Adaptive Methods for Real-World Domain Generalization

  • Adaptive methods for domain generalization
  • 动态算法，用于domain generalization

• 20180701 arXiv 做迁移时，只用source数据，不用target数据训练：Generalizing to Unseen Domains via Adversarial Data Augmentation

• 201711 ICLR-18 GENERALIZING ACROSS DOMAINS VIA CROSS-GRADIENT TRAINING

  • 不同于以往的工作，本文运用贝叶斯网络建模label和domain的依赖关系，抓住training、inference 两个过程，有效引入domain perturbation来实现domain adaptation。

• ICLR-18 generalizing across domains via cross-gradient training

• 20181106 PRCV-18 Domain Attention Model for Domain Generalization in Object Detection

  • Adding attention for domain generalization
  • 在domain generalization中加入了attention机制

• 20181225 WACV-19 Multi-component Image Translation for Deep Domain Generalization

  • Using GAN generated images for domain generalization
  • 用GAN生成的图像进行domain generalization

• 20180724 arXiv Domain Generalization via Conditional Invariant Representation

  • Using Conditional Invariant Representation for domain generalization
  • 生成条件不变的特征表达，用于domain generalization问题

• 20181212 arXiv Beyond Domain Adaptation: Unseen Domain Encapsulation via Universal Non-volume Preserving Models

  • Domain generalization method
  • 一种针对于unseen domain的学习方法

• 20171210 AAAI-18 Learning to Generalize: Meta-Learning for Domain Generalization

  • 将Meta-Learning与domain generalization结合的文章，可以联系到近期较为流行的few-shot learning进行下一步思考。

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Source-free domain adaptation

• Visual Prompt Tuning for Test-time Domain Adaptation [arxiv]

  • VPT for test-time adaptation 用prompt tuning进行test-time DA

• Active Source Free Domain Adaptation

  • Active source-free DA 主动学习-无源域DA

• NeurIPS-21 Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data

  • Source-free domain adaptation using constrastive learning
  • 无源域数据的DA，利用对比学习

• 20200706 Domain Adaptation without Source Data

• 20200629 ICML-20 Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation - Source-free adaptation - 在adaptation过程中不访问source data

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Multi-source domain adaptation

• Open-Set Crowdsourcing using Multiple-Source Transfer Learning

  • Open-set crowdsourcing using multiple-source transfer learning
  • 使用多源迁移进行开放集的crowdsourcing

• BMVC-21 Domain Attention Consistency for Multi-Source Domain Adaptation

  • Multi-source domain adaptation using attention consistency
  • 用attention一致性进行多源的domain adaptation

• CVPR-21 Wasserstein Barycenter for Multi-Source Domain Adaptation

  • Use Wasserstein Barycenter for multi-source domain adaptation
  • 利用Wasserstein Barycenter进行DA

• 20210430 Graphical Modeling for Multi-Source Domain Adaptation

  • Graphical models for multi-source DA
  • 用概率图模型进行多源领域自适应

• 20210430 Unsupervised Multi-Source Domain Adaptation for Person Re-Identification

  • ReID using multi-source DA
  • 用多源领域自适应进行ReID任务

• 20200427 TriGAN: Image-to-Image Translation for Multi-Source Domain Adaptation

  • A cycle-gan style multi-source DA
  • 类似于cyclegan的多源领域适应

• 20190902 AAAI-19 Aligning Domain-Specific Distribution and Classifier for Cross-Domain Classification from Multiple Sources

  • Multi-source domain adaptation using both features and classifier adaptation
  • 利用特征和分类器同时适配进行多源迁移，效果很好

• 20181212 AIKP Multi-source Transfer Learning

  • Multi-source transfer

• 20181207 arXiv Moment Matching for Multi-Source Domain Adaptation

  • Moment matching and propose a new large dataset for domain adaptation
  • 提出一种moment matching的网络，并且提出一种新的domain adaptation数据集，很大

• CoRR abs/1711.09020 (2017) StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

• 20180524 arXiv 探索了Multi-source迁移学习的一些理论：Algorithms and Theory for Multiple-Source Adaptation

• 20181117 AAAI-19 Robust Optimization over Multiple Domains

  • Optimization on multi domains
  • 针对多个domain建模并优化

• 20180912 arXiv Multi-target Unsupervised Domain Adaptation without Exactly Shared Categories

  • Multi-target domain adaptation
  • 多目标的domain adaptation

• 20180316 arXiv 用optimal transport解决domain adaptation中类别不平衡的问题：Optimal Transport for Multi-source Domain Adaptation under Target Shift

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Heterogeneous transfer learning

• 20190717 AAAI Heterogeneous Transfer Learning via Deep Matrix Completion with Adversarial Kernel Embedding

  • Transfer Learning via Deep Matrix Completion with Adversarial Kernel Embedding
  • 异构迁移学习中用对抗核嵌入的深度矩阵

• 20190829 ACMMM-19 Heterogeneous Domain Adaptation via Soft Transfer Network

  • Soft-mmd loss in heterogeneous domain adaptation
  • 异构迁移学习中用soft-mmd loss

• 20181113 ACML-18 Unsupervised Heterogeneous Domain Adaptation with Sparse Feature Transformation

  • Heterogeneous domain adaptation
  • 异构domain adaptation

• 20180901 TKDE A General Domain Specific Feature Transfer Framework for Hybrid Domain Adaptation

  • Hybrid DA: special case in Heterogeneous DA
  • 提出一种新的混合DA问题和方法

• 20180606 arXiv 一篇最近的对非对称情况下的异构迁移学习综述：Asymmetric Heterogeneous Transfer Learning: A Survey

• 20180403 Neural Processing Letters-18 异构迁移学习：Label Space Embedding of Manifold Alignment for Domain Adaption

• 20180105 arXiv 异构迁移学习 Heterogeneous transfer learning

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Online transfer learning

• CVPR-22 workshop Online Unsupervised Domain Adaptation for Person Re-identification

  • Online domain adaptation for REID 在线adaptation

• Mixture of basis for interpretable continual learning with distribution shifts

  • Incremental learning with mixture of basis
  • 用mixture of domains进行增量学习

• 20180326 考虑主动获取label的budget情况下的在线迁移学习：Online domain adaptation by exploiting labeled features and pro-active learning

• 20180128 第一篇在线迁移学习的文章，发表在ICML-10上，系统性地定义了在线迁移学习的任务，给出了进行在线同构和异构迁移学习的两种学习模式。Online Transfer Learning

  • 扩充的期刊文章发在2014年的AIJ上：Online Transfer Learning
  • 我的解读
  • 文章代码：OTL

• 20180126 两篇在线迁移学习：

  • Online transfer learning by leveraging multiple source domains
  • Online Heterogeneous Transfer by Hedge Ensemble of Offline and Online Decisions

• 20180126 TKDE-17 同时有多个同构和异构源域时的在线迁移学习：Online Transfer Learning with Multiple Homogeneous or Heterogeneous Sources

• KIS-17 Online transfer learning by leveraging multiple source domains 提出一种综合衡量多个源域进行在线迁移学习的方法。文章的related work是很不错的survey。

• CIKM-13 OMS-TL: A Framework of Online Multiple Source Transfer Learning 第一次在mulitple source上做online transfer，也是用的分类器集成。

• ICLR-17 ONLINE BAYESIAN TRANSFER LEARNING FOR SEQUENTIAL DATA MODELING 用贝叶斯的方法学习在线的HMM迁移学习模型，并应用于行为识别、睡眠监测，以及未来流量分析。

• KDD-14 Scalable Hands-Free Transfer Learning for Online Advertising 提出一种无参数的SGD方法，预测广告量

• TNNLS-17 Online Feature Transformation Learning for Cross-Domain Object Category Recognition 在线feature transformation方法

• ICPR-12 Online Transfer Boosting for Object Tracking 在线transfer 样本

• TKDE-14 Online Feature Selection and Its Applications 在线特征选择

• AAAI-15 Online Transfer Learning in Reinforcement Learning Domains 应用于强化学习的在线迁移学习

• AAAI-15 Online Boosting Algorithms for Anytime Transfer and Multitask Learning 一种通用的在线迁移学习方法，可以适配在现有方法的后面

• IJSR-13 Knowledge Transfer Using Cost Sensitive Online Learning Classification 探索在线迁移方法，用样本cost

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Zero-shot / few-shot learning

• Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation Learning

  • Few-shot DA for unsupervised constrastive learning 小样本DA用于无监督对比学习

• Interpretable Concept-based Prototypical Networks for Few-Shot Learning

  • Concept-based prototypical network for few-shot learning
  • 基于概念的原型网络用于小样本学习

• How Well Do Self-Supervised Methods Perform in Cross-Domain Few-Shot Learning?

  • Self-supervised learning for cross-domain few-shot
  • 自监督用于跨领域小样本

• 20181128 arXiv One Shot Domain Adaptation for Person Re-Identification

  • One shot learning for REID
  • One shot for再识别

• 20210426 Few-shot Continual Learning: a Brain-inspired Approach

  • Few-shot continual learning
  • 小样本持续学习

• 20201203 How to fine-tune deep neural networks in few-shot learning?

  • 对few-shot任务如何fine-tune深度网络？

• 20201116 Filter Pre-Pruning for Improved Fine-tuning of Quantized Deep Neural Networks

  • 量子神经网络中的finetune

• 20200608 ICML-20 Few-Shot Learning as Domain Adaptation: Algorithm and Analysis

  • Using domain adaptation to solve the few-shot learning

• 20200408 ICLR-20 A Baseline for Few-Shot Image Classification - A simple finetune+entropy minimization approach with strong baseline - 一个微调+最小化熵的小样本学习方法，结果很强

• 20200405 ICCV-19 Variational few-shot learning

  • Variational few-shot learning
  • 变分小样本学习

• 20200405 ICLR-20 A baseline for few-shot image classification

  • A simple but powerful baseline for few-shot image classification
  • 一个简单但是很有效的few-shot baseline

• 20200324 IEEE TNNLS Few-Shot Learning with Geometric Constraints

  • Few-shot learning with geometric constraints
  • 用了一些几何约束进行小样本学习

• 20190813 arXiv Domain-Specific Embedding Network for Zero-Shot Recognition

  • Domain-specific embedding network for zero-shot learning
  • 领域自适应的zero-shot learning

• 20190401 TIp-19 Few-Shot Deep Adversarial Learning for Video-based Person Re-identification

  • Few-shot deep adversarial learning
  • Few-shot对抗学习

• 20190305 arXiv Zero-Shot Task Transfer

  • Zero-shot task transfer
  • Zero-shot任务迁移学习

• 20190221 arXiv Adaptive Cross-Modal Few-Shot Learning

  • Adaptive cross-modal few-shot learning
  • 跨模态的few-shot

• 20180612 CVPR-18 泛化的Zero-shot learning：Generalized Zero-Shot Learning via Synthesized Examples

• 20181106 arXiv Zero-Shot Transfer VQA Dataset

  • English: A dataset for zero-shot VQA transfer
  • 中文：一个针对zero-shot VQA的迁移学习数据集

• 20171222 NIPS 2017 用adversarial网络，当target中有很少量的label时如何进行domain adaptation：Few-Shot Adversarial Domain Adaptation

• 20181225 arXiv Learning Compositional Representations for Few-Shot Recognition

  • Few-shot recognition

• 20181127 WACV-19 Self Paced Adversarial Training for Multimodal Few-shot Learning

  • Multimodal training for single modal testing
  • 用多模态数据针对单一模态进行迁移

• 20180728 arXiv Meta-learning autoencoders for few-shot prediction

  • Using meta-learning for few-shot transfer learning
  • 用元学习进行迁移学习

• 20171216 arXiv Zero-Shot Deep Domain Adaptation

  • 当target domain的数据不可用时，如何用相关domain的数据进行辅助学习？

• 20191204 arXiv MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification

  • Task adaptive structure for few-shot learning
  • 目标自适应的结构用于小样本学习

• 20190409 ICLR-19 A Closer Look at Few-shot Classification

  • Give some important conclusions on few-shot classification
  • 在few-shot上给了一些有用的结论

• 20190401 IJCNN-19 Zero-shot Image Recognition Using Relational Matching, Adaptation and Calibration

  • Zero-shot image recognition
  • 零次学习的图像识别

• 20171022 ICCVW-17 Zero-shot learning posed as a missing data problem

  • 算法首先学习 semantic embeddings 的结构性知识，利用学习到的知识和已知类的 image features 合成未知类的 image features。再利用无标记的未知类数据对合成数据进行修正。 算法假设未知类数据呈混合高斯分布，用 GMM-EM 算法进行无监督修正。

• 20180516 arXiv-18 A Large-scale Attribute Dataset for Zero-shot Learning

  • 传统 ZSL 数据集（如 AwA, CUB）存在规模小，属性标注不丰富等问题。本文提出一个新的属性数据集 LAD 用于测试零样本学习算法。新数据集包含 230 类， 78,017 张图片，标注了 359 种属性。基于此数据集举办了 AI Challenger 零样本学习竞赛。 110+ 支来自海内外的参赛队伍提交了成绩。

• 20180710 ICML-18 MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning

  • 针对 L1 （欠拟合） 和 L2 （无特征选择、有偏） 正则项存在的问题，提出 MSplit LBI 用于同时实现特征选择和密集估计。在 Few-shot Learning 和 Zero-shot Learning 两个问题上进行了实验。实验表明 MSplit LBI 由优于 L1 和 L2。针对 ZSL 进行了特征可视化实验。

• 20190108 WACV-19 Zero-shot Learning via Recurrent Knowledge Transfer

  • 基于样本合成的零样本学习算法通常将 semantic embeddings 的知识迁移到 image features 以实现 ZSL。然而，这种 training 和 testing space 的不一致，会导致这种迁移失效。因此，本文提出 Space Shift Problem，并针对此问题，提出一种（在 image feature space 和 semantic embedding space 之间）递归传递知识的解决方案。

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Multi-task learning

• Gap Minimization for Knowledge Sharing and Transfer

  • Multitask learning with gap minimization
  • 用于多任务学习的gap minimization方法

• 20190806 KDD-19 Relation Extraction via Domain-aware Transfer Learning

  • Relation extraction using transfer learning for knowledge base construction
  • 利用迁移学习进行关系抽取

• 20190531 arXiv Multi-task Learning in Deep Gaussian Processes with Multi-kernel Layers

  • Multi-task learning in deep Gaussian process
  • 深度高斯过程中的多任务学习

• 20200927 Knowledge Distillation for Multi-task Learning

  • 针对多任务学习的知识蒸馏

• 20200914 ECML-PKDD-20 Towards Interpretable Multi-Task Learning Using Bilevel Programming

  • 用bilevel programming解释多任务学习

• 20191202 arXiv AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning

  • Learning what to share for multi-task learning
  • 对多任务学习如何share

• 20191125 AAAI-20 Adaptive Activation Network and Functional Regularization for Efficient and Flexible Deep Multi-Task Learning

  • Adaptive activation network for deep multi-task learning
  • 自适应的激活网络用于深度多任务学习

• 20191015 arXiv Gumbel-Matrix Routing for Flexible Multi-task Learning

  • Effective method for flexible multi-task learning
  • 一种很有效的方法用于多任务学习

• 20190718 arXiv Task Selection Policies for Multitask Learning

  • Task selection in multitask learning
  • 在多任务学习中的任务选择机制

• 20190509 FG-19 Multi-task human analysis in still images: 2D/3D pose, depth map, and multi-part segmentation

  • Multi-task human analysis in still images
  • 多任务人体静止图像分析

• 20190409 NAACL-19 AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning

  • Automatic Task Selection and Mixing in Multi-Task Learning
  • 多任务学习中自动任务选择和混淆

• 20190409 TNNLS-19 Heterogeneous Multi-task Metric Learning across Multiple Domains

  • Heterogeneous Multi-task Metric Learning across Multiple Domains
  • 在多个领域之间进行异构多任务度量学习

• 20190409 NeurIPS-18 Synthesized Policies for Transfer and Adaptation across Tasks and Environments

  • Transfer across tasks and environments
  • 通过任务和环境之间进行迁移

• 20190408 ICMR-19 Learning Task Relatedness in Multi-Task Learning for Images in Context

  • Using task relatedness in multi-task learning
  • 在多任务学习中学习任务之间的相关性

• 20190408 CVPR-19 End-to-End Multi-Task Learning with Attention

  • End-to-End Multi-Task Learning with Attention
  • 基于attention的端到端的多任务学习

• 20190401 arXiv Many Task Learning with Task Routing

  • From multi-task leanring to many-task learning
  • 许多任务同时学习

• 20190324 arXiv A Principled Approach for Learning Task Similarity in Multitask Learning

  • Provide some theoretical analysis of the similarity learning in multi-task learning
  • 为多任务学习中的相似度学习提供了一些理论分析

• 20181128 arXiv A Framework of Transfer Learning in Object Detection for Embedded Systems

  • A Framework of Transfer Learning in Object Detection for Embedded Systems
  • 一个用于嵌入式系统的迁移学习框架

• 20181012 NIPS-18 Multi-Task Learning as Multi-Objective Optimization

  • Solve the multi-task learning as a multi-objective optimization problem
  • 将多任务问题看成一个多目标优化问题进行求解

• 20181008 PSB-19 The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data

  • Evaluate the effectiveness of multitask learning for phenotyping
  • 评估多任务学习对于表型的作用

• 20180828 arXiv Self-Paced Multi-Task Clustering

  • Multi-task clustering
  • 多任务聚类

• 20180622 arXiv 探索了多任务迁移学习中的不确定性：Uncertainty in Multitask Transfer Learning

• 20180524 arXiv 杨强团队、与之前的learning to learning类似，这里提供了一个从经验中学习的learning to multitask框架：Learning to Multitask

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Transfer reinforcement learning

• Multi-Agent Transfer Learning in Reinforcement Learning-Based Ride-Sharing Systems

  • Multi-agent transfer in RL
  • 在RL中的多智能体迁移

• NeurIPS-21 workshop Component Transfer Learning for Deep RL Based on Abstract Representations

  • Deep transfer learning for RL
  • 深度迁移学习用于强化学习

• Xi-Learning: Successor Feature Transfer Learning for General Reward Functions

  • General reward function transfer learning in RL
  • 在强化学习中general reward function的迁移学习

• NeurIPS-21 Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning

  • Domain adaptation in reinforcement learning
  • 在强化学习中应用domain adaptation

• Understanding Domain Randomization for Sim-to-real Transfer

  • Understanding domain randomizationfor sim-to-real transfer
  • 对强化学习中的sim-to-real transfer进行理论上的分析

• 20191214 arXiv Does Knowledge Transfer Always Help to Learn a Better Policy?

  • Transfer learning in reinforcement learning

• 20191212 AAAI-20 Transfer value iteration networks

  • Transferred value iteration networks

• 20190821 arXiv Transfer in Deep Reinforcement Learning using Knowledge Graphs

  • Use knowledge graph to transfer in reinforcement learning
  • 用知识图谱进行强化迁移

• 20190320 arXiv Learning to Augment Synthetic Images for Sim2Real Policy Transfer

  • Augment synthetic images for sim to real policy transfer
  • 学习对于策略迁移如何合成图像

• 20190305 arXiv [Sim-to-Real Transfer for Biped Locomotion]

  • Transfer learning for robot locomotion
  • 用迁移学习进行机器人定位

• 20190220 arXiv DIViS: Domain Invariant Visual Servoing for Collision-Free Goal Reaching

  • Transfer learning for robot reinforcement learning
  • 迁移学习用于机器人的强化学习目标搜寻

• 20181212 NeurIPS-18 workshop Efficient transfer learning and online adaptation with latent variable models for continuous control

  • Reinforcement transfer learning with latent models
  • 隐变量模型用于迁移强化学习的控制

• 20181128 arXiv Hardware Conditioned Policies for Multi-Robot Transfer Learning

  • Hardware Conditioned Policies for Multi-Robot Transfer Learning
  • 多个机器人之间的迁移学习

• 20180926 arXiv Target Transfer Q-Learning and Its Convergence Analysis

  • Analyze the risk of transfer q-learning
  • 提供了在�Q learning的任务迁移中一些理论分析

• 20180926 arXiv Domain Adaptation in Robot Fault Diagnostic Systems

  • Apply domain adaptation in robot fault diagnostic system
  • 将domain adaptation应用于机器人故障检测系统

• 20180912 arXiv VPE: Variational Policy Embedding for Transfer Reinforcement Learning

  • Policy transfer in reinforcement learning
  • 增强学习中的策略迁移

• 20180909 arXiv Transferring Deep Reinforcement Learning with Adversarial Objective and Augmentation

  • deep + adversarial + reinforcement learning transfer
  • 深度对抗迁移学习用于强化学习

• 20180530 ICML-18 强化迁移学习：Importance Weighted Transfer of Samples in Reinforcement Learning

• 20180524 arXiv 用深度强化学习的方法学习domain adaptation中的采样策略：Learning Sampling Policies for Domain Adaptation

• 20180516 arXiv 探索了强化学习中的任务迁移：Adversarial Task Transfer from Preference

• 20180413 NIPS-17 基于后继特征迁移的强化学习：Successor Features for Transfer in Reinforcement Learning

• 20180404 IEEE TETCI-18 用迁移学习来玩星际争霸游戏：StarCraft Micromanagement with Reinforcement Learning and Curriculum Transfer Learning

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Transfer metric learning

• 20190515 TNNLS-19 A Distributed Approach towards Discriminative Distance Metric Learning

  • Discriminative distance metric learning
  • 分布式度量学习

• 20190409 TNNLS-19 Heterogeneous Multi-task Metric Learning across Multiple Domains

  • Heterogeneous Multi-task Metric Learning across Multiple Domains
  • 在多个领域之间进行异构多任务度量学习

• 20190409 PAMI-19 Transferring Knowledge Fragments for Learning Distance Metric from A Heterogeneous Domain

  • Heterogeneous transfer metric learning by transferring fragments
  • 通过迁移知识片段来进行异构迁移度量学习

• 20190409 arXiv Decomposition-Based Transfer Distance Metric Learning for Image Classification

  • Transfer metric learning based on decomposition
  • 基于特征向量分解的迁移度量学习

• 20181012 arXiv Transfer Metric Learning: Algorithms, Applications and Outlooks

  • A survey on transfer metric learning
  • 一篇迁移度量学习的综述

• 20180622 arXiv 基于深度迁移学习的度量学习：DEFRAG: Deep Euclidean Feature Representations through Adaptation on the Grassmann Manifold

• 20181117 arXiv Distance Measure Machines

  • Machines that measures distances
  • 衡量距离的算法

• 20180605 KDD-10 迁移度量学习：Transfer metric learning by learning task relationships

• 20180606 arXiv 将流形和统计信息联合起来构成一个domain adaptation框架：A Unified Framework for Domain Adaptation using Metric Learning on Manifolds

• 20180605 CVPR-15 深度度量迁移学习：Deep metric transfer learning

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Federated transfer learning

• FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learning [arxiv]

  • Fast generalization for federated CLIP 在联邦中进行快速的CLIP训练

• Federated Semi-Supervised Domain Adaptation via Knowledge Transfer

  • Federated semi-supervised DA 联邦半监督DA

• FL-IJCAI-22 MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare

  • MetaFed: a new form of federated learning
  • 联邦之联邦学习、新范式

• Interspeech-22 Decoupled Federated Learning for ASR with Non-IID Data

  • Decoupled federated learning for non IID
  • 解耦的联邦架构用于Non-IID语音识别

• Test-Time Robust Personalization for Federated Learning

  • Test-time robust personalization for FL
  • 测试时鲁棒联邦学习

• IEEE TNNLS-22 Towards Personalized Federated Learning

  • A survey on personalized federated learning
  • 一个关于个性化联邦学习的综述

• Improving Generalization in Federated Learning by Seeking Flat Minima

  • Seeking flat minima for domain generalization in federated learning
  • 通过寻找平坦值进行联邦学习领域泛化

• SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence

  • Personalized federated learning
  • 个性化联邦学习

• NeurIPS-21 Parameterized Knowledge Transfer for Personalized Federated Learning

  • personalized group knowledge transfer training
  • 个性化群体知识迁移

• ICML-21 Federated Continual Learning with Weighted Inter-client Transfer

  • Federated Weighted Inter-client Transfer (FedWeIT) for Federated Continual Learning
  • 联邦加权客户端间传输方法，用于联邦持续学习

• SIGIR-21 FedCT: Federated Collaborative Transfer for Recommendation

  • Federated learning for cross-domain recommendation
  • 使用联邦迁移学习执行跨域推荐任务

• KDD-21 Federated Adversarial Debiasing for Fair and Transferable Representations

  • Federated Adversarial DEbiasing (FADE)
  • 通过对抗性学习对联邦学习过程去除偏见

• Federated Learning with Adaptive Batchnorm for Personalized Healthcare

  • Federated learning with adaptive batchnorm
  • 用自适应BN进行个性化联邦学习

• FedZKT: Zero-Shot Knowledge Transfer towards Heterogeneous On-Device Models in Federated Learning

  • Zero-shot transfer in heterogeneous federated learning
  • 零次迁移用于联邦学习

• Federated Multi-Task Learning under a Mixture of Distributions

  • Federated multi-task learning
  • 联邦多任务学习

• NeurIPS-20 Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge

  • Group knowledge transfer training
  • 群体知识迁移

• Fine-tuning is Fine in Federated Learning

  • Finetuning in federated learning
  • 在联邦学习中进行finetune

• Federated Multi-Target Domain Adaptation

  • Federated multi-target DA
  • 联邦学习场景下的多目标DA

• 20190909 IJCAI-FML-19 FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare

  • The first work on federated transfer learning for wearable healthcare
  • 第一个将联邦迁移学习用于可穿戴健康监护的工作

• 20180605 arXiv 解决federated learning中的数据不同分布的问题：Federated Learning with Non-IID Data

• 20190301 NeurIPS-18 workshp One-Shot Federated Learning

  • One-shot federated learning

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Lifelong transfer learning

• Complementary Domain Adaptation and Generalization for Unsupervised Continual Domain Shift Learning [arxiv]

  • Continual domain shift learning using adaptation and generalization 使用 adaptation和DG进行持续分布变化的学习

• TMLR'23 Learn, Unlearn and Relearn: An Online Learning Paradigm for Deep Neural Networks [arxiv]

  • A framework for online learning 一个在线学习的框架

• NeurIPS'22 Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer [arxiv]

  • Continual learning with backward knowledge transfer 反向知识迁移的持续学习

• Mixture of basis for interpretable continual learning with distribution shifts

  • Incremental learning with mixture of basis
  • 用mixture of domains进行增量学习

• 20101008 arXiv Concept-drifting Data Streams are Time Series; The Case for Continuous Adaptation

  • Continuous adaptation for time series data
  • 对时间序列进行连续adaptation

• 20191011 arXiv Learning to Remember from a Multi-Task Teacher

  • Dealing with the catastrophic forgetting during sequential learning
  • 在序列学习时处理灾难遗忘

• 20191029 Adversarial Feature Alignment: Avoid Catastrophic Forgetting in Incremental Task Lifelong Learning

  • Avoid catastrophic forgeeting in incremental task lifelong learning
  • 在终身学习中避免灾难遗忘

• 20200706 [ICML-20] Continuously Indexed Domain Adaptation

• 20210716 TPAMI-21 Lifelong Teacher-Student Network Learning

  • Lifelong distillation
  • 持续的知识蒸馏

• 20210716 ICML-21 Continual Learning in the Teacher-Student Setup: Impact of Task Similarity

  • Investigating task similarity in teacher-student learning
  • 调研在continual learning下teacher-student learning问题的任务相似度

• 20190912 NeurIPS-19 Meta-Learning with Implicit Gradients

  • Meta-learning with implicit gradients
  • 隐式梯度的元学习

• 20180323 arXiv 终身迁移学习与增量学习结合：Incremental Learning-to-Learn with Statistical Guarantees

• 20180111 arXiv 一种新的终身学习框架，与L2T的思路有一些类似 Lifelong Learning for Sentiment Classification

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Safe transfer learning

• ICSE-22 ReMoS: Reducing Defect Inheritance in Transfer Learning via Relevant Model Slicing | Code | Blog | Video

  • Safe transfer learning by reducing defect inheritance
  • 安全迁移学习的最新工作

• CVPR workshop-21 Renofeation: A Simple Transfer Learning Method for Improved Adversarial Robustness

  • Improve adversarial robustness of transfer learning models
  • 提高迁移学习对于adversarial robustness的鲁棒性

• ICLR-20 A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learning

  • Softmax layer is easy to get attacked
  • 设计实验来攻击迁移学习的softmax layer

• RAID'18 Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks

  • Finetune and prune the weights against backdoor attack
  • 在finetune过程中剪枝来预防后门攻击

• ACM CCS-18 Model-Reuse Attacks on Deep Learning Systems

  • Model-resuse attack on transfer learning models
  • 设计实验来攻击迁移学习的预训练模型

• USENIX Security-18 With Great Training Comes Great Vulnerability: Practical Attacks against Transfer Learning

  • First work to design experiments to attack pretrained models
  • 第一个设计实验来攻击预训练模型的工作

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Transfer learning applications

See HERE for a full list of transfer learning applications.