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This repo contains annotated research papers that I found really good and useful

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Annotate Research Papers

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Why annotated papers?

Do you love reading research papers? Or do you find reading papers intimidating? Or are you looking for annotated research papers that are much easier to understand?

If you are in any of the categories listed above, then you have arrived at the right place. I spend a lot of time reading papers. It is a crucial part of my ML work. If you want to do research or you want to be a better ML engineer, then you should read papers. This habit of reading papers will help you to remain updated with the field.

Note: I am a pen-paper guy. Nothing beats that pen-paper reading experience, but in the ongoing scenarios (pandemic, lockdown, etc.), I am not able to print the papers. Taking this as an opportunity to share my thought process, I will be sharing the annotated research papers in this repo. The order of the papers won't strictly be according to the timeline on arXiv. Sometimes I put a paper on hold and read it after a while.

PS: I cannot annotate all the papers I read, but if I liked one, then that will be uploaded here. Also, there will be blog posts for a few research papers that are really important.

Table of Contents

Field Category Annotated Paper
Computer Vision Adaptive Risk Minimization Abstract
Axial DeepLab Code Abstract
ConvNext Code Abstract
EfficientNetsV2 Code Abstract
Supervised Flow-edge Guided Video Completion Code Abstract
Is Batch Norm Unique? Abstract
Knowledge Distillation: A good teacher is patient and consistent Code Abstract
RandConv Code Abstract
Polyloss Code Abstract
Scaling Down Deep Learning Code Abstract
Supervised Contrastive Learning Code Abstract
Vision Transformer Code Abstract
Are all negatives created equal in contrastive instance discrimination? Abstract
Towards Domain-Agnostic Contrastive Learning Abstract
Self-Supervised Emerging Properties in Self-Supervised Vision Transformers Code Abstract
Masked Autoencoders Code Abstract
Swav Code Abstract
What Should Not Be Contrastive in Contrastive Learning Abstract
Semi-Supervised CoMatch Code Abstract
Diffusion Models Understanding Diffusion Models Abstract
On the Importance of Noise Scheduling for Diffusion Models Abstract
GANs CycleGan Code Abstract
Interpretability and Explainability What is being transferred in transfer learning? Code Abstract
Explaining in Style Code Abstract
NLP Do Language Embeddings Capture Scales? Abstract
mSLAM Abstract
Cramming Abstract
Speech SpeechStew Abstract
mSLAM Abstract
WhisperX Code Abstract
Others Multi-Task Self-Training for Learning General Representations Abstract
Decoder Denoising Pretraining for Semantic Segmentation Abstract

Community Contributions

Note: The annotated papers in this section are contributed by the community. As I cannot verify the annotation for each paper, I will lay out certain guidelines for annotations so that every annotated paper has same sections at least

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This repo contains annotated research papers that I found really good and useful

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