Annotate Research Papers

Why annotated papers?

Do you love reading research papers? Or do you want to read more research papers but find them 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 the 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 you can expect all the interesting papers to be uploaded here.

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
		Segment Anything		Abstract
		Supervised Contrastive Learning	Code	Abstract
		Vision Transformer	Code	Abstract
		Gaze-LLE	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
		Decoder Denoising Pretraining		Abstract
		Masked Autoencoders	Code	Abstract
		Swav	Code	Abstract
		What Should Not Be Contrastive in Contrastive Learning		Abstract
		Vision Transformers need Registers		Abstract
	Semi-Supervised	CoMatch	Code	Abstract
Diffusion Models		Understanding Diffusion Models		Abstract
		On the Importance of Noise Scheduling for Diffusion Models		Abstract
		Emergent Correspondence from 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
		Shortened Llama		Abstract
		CoPE		Abstract
		LLMs cannot plan but can help planning		Abstract
		Mixture of A Million Experts		Abstract
		Agent Workflow Memory	Code	Abstract
		What Matters for Model Merging at Scale?		Abstract
		Matryoshka Quantization		Abstract
		Scaling Laws Are Unreliable for Downstream Tasks		Abstract
Speech		SpeechStew		Abstract
		mSLAM		Abstract
		WhisperX	Code	Abstract
MLLMs		VCoder: Versatile Vision Encoder for MLLMs	Code	Abstract
		Sigmoid Loss for Image-Text Pretraining		Abstract
		MobileCLIP		Abstract
		MM1		Abstract
		Ferretv2		Abstract
		VisualFactChecker		Abstract
		JanusFLow	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 the guidelines for annotations so that every annotated paper has similar annotated sections.