Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
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Updated
May 10, 2024 - Python
Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
[ICLR-2020] Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification.
[CVPR22] Official Implementation of DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation
PyTorch open-source toolbox for unsupervised or domain adaptive object re-ID.
pytorch implementation for Contrastive Adaptation Network
[ECCV22] Official Implementation of HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation
[TKDD 2023] AdaTime: A Benchmarking Suite for Domain Adaptation on Time Series Data
Implementation of ECCV 2020 paper "Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detector"
Self-supervised Augmentation Consistency for Adapting Semantic Segmentation (CVPR 2021)
Code for ICML2020 "Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation"
NeurIPS 2023: Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation
An official implementation of ICML 2022 paper "Learning Domain Adaptive Object Detection with Probabilistic Teacher"."
Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to Night
Deep learning research implemented on notebooks using PyTorch.
Official Detectron2 implementation of DA-RetinaNet of our Image and Vision Computing 2021 work 'An unsupervised domain adaptation scheme for single-stage artwork recognition in cultural sites'
Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling
Official implementation of "Align and Distill: Unifying and Improving Domain Adaptive Object Detection"
Detectron2 implementation of DA-Faster R-CNN, Domain Adaptive Faster R-CNN for Object Detection in the Wild
This is a PyTorch implementation of the Unsupervised Domain Adaptation method proposed in the paper Deep CORAL: Correlation Alignment for Deep Domain Adaptation. Baochen Sun and Kate Saenko (ECCV 2016).
[AAAI 2024] Prompt-based Distribution Alignment for Unsupervised Domain Adaptation
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