rlatjcj/paper-code-review

Detail or simple reviews of papers. Codes of papers were separated into a new organization on the link below.

🎲 Paper-code-review

📖 Table of whole paper reviews 👉🏻 link

👩🏻‍💻 Organization with custom implementation codes 👉🏻 link

Transformer

From	Year	Authors	Paper	Institution	url
ICLR	2021	X. Zhu and W. Su et al.	Deformable DETR: Deformable Transformers for End-to-End Object Detection	SenseTime Research, Univ. of Science and Technology of China, and The Chinese Univ. of Hong Kong	[official code]
ICLR	2021	K. Choromanski, V. Likhosherstov, D. Dohan, X. Song, A. Gane, T. Sarlos, P. Hawkins, and J. Davis et al.	Rethinking Attention with Performers	Google, Univ. of Cambridge, Deepmind, and Alan Turing Institute	[official code]
Arxiv	2020	S. Wang et al.	Linformer: Self-Attention with Linear Complexity	Facebook AI	-
Arxiv	2020	H. Touvron et al.	Training Data-efficient Image Transformer & Distillation through Attention	Facebook AI Research (FAIR) and Sorbonne Univ.	[official code] [summary]
Arxiv	2020	A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, and X. Zhai et al.	An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale	Google Research	[official code] [summary]
ECCV	2020	N. Carion et al.	End-to-End Object Detection with Transformers	Facebook AI Research (FAIR)	[official code] [custom code] [summary]
ICLR	2020	N. Kitaev and L. Kaiser et al.	Reformer: The Efficient Transformer	U.C. Berkeley and Google Research	[official code]
NeurIPS	2017	A. Vaswani et al.	Attention Is All You Need	Google Brain, Google Research, and Univ. of Toronto	[official code]

Self-supervised learning

From	Year	Authors	Paper	Institution	url
Arxiv	2020	X. Chen et al.	Exploring Simple Siamese Representation Learning	Facebook AI Research (FAIR)	[custom code] [summary]
Arxiv	2020	Z. Xie and Y. Lin et al.	Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning	Tsinghua Univ., Xi'an Jiaotong Univ. and Microsoft Research Asia	[custom code] [summary]
Arxiv	2020	X. Chen et al.	Improved Baselines with Momentum Contrastive Learning	Facebook AI Research (FAIR)	[official code] [summary]
Arxiv	2020	J. Grill, F. Strub, F. Altche, C. Tallec, and P. H. Richemond et al.	Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning	DeepMind, Imperial College	[official code] [summary]
Arxiv	2020	P. Khosla et al.	Supervised Contrastive Learning	Google Research	[official code] [custom code] [summary]
CVPR	2020	K. He et al.	Momentum Contrast for Unsupervised Visual Representation Learning	Facebook AI Research (FAIR)	[official code] [custom code] [summary]

Semi-supervised learning

From	Year	Authors	Paper	Institution	url
Arxiv	2020	K. Sohn et al.	FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence	Google Research	[official code] [summary]
CVPR	2020	D. Wang et al.	FocalMix: Semi-Supervised Learning for 3D Medical Image Detection	Peking University, Yizhun Medical AI	[summary]
ICLR	2020	D. Berthelot et al.	ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring	Google Research and Google Cloud AI	[official code]
NeurIPS	2019	D. Berthelot et al.	MixMatch: A Holistic Approach to Semi-Supervised Learning	Google Research	[official code] [summary]
Arxiv	2019	Q. Xie et al.	Unsupervised Data Augmentation for Consistency Training	Google Research, Carnegie Mellon University	[official code] [summary] [ppt]

Knowledge distillation

From	Year	Authors	Paper	Institution	url
CVPR	2020	S. Yun and J. Park et al.	Regularizing Class-wise Predictions via Self-knowledge Distillation	KAIST	[official code] [summary] [ppt]
CVPR	2020	Y. Liu et al.	Search to Distill: Pearls are Everywhere but not the Eyes	Google AI, Google Brain	[summary]
ICCV	2019	L. Zhang et al.	Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation	Tsinghua University	[official code] [summary]
ICCV	2019	B. Heo et al.	A Comprehensive Overhaul of Feature Distillation	NAVER Corp, Seoul National University	[official code]
NeurIPS	2018	X. Wang et al.	KDGAN: Knowledge Distillation with Generative Adversarial Networks	University of Melbourne	[official code] [summary] [ppt]
ICML	2018	S. Srinivas et al.	Knowledge Transfer with Jacobian Matching	Idiap Research Institute & EPFL	[summary]

Modeling & NAS

From	Year	Authors	Paper	Institution	url
ICLR	2021	T. Nguyen et al.	Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth	Google Research	-
CVPR	2020	I. Radosavovic et al.	Designing Network Design Spaces	Facebook AI Research (FAIR)	[official code] [custom code] [summary] [ppt]
CVPR	2019	T. He et al.	Bag of Tricks for Image Classification with Convolutional Neural Networks	Amazon Web Services	-
ICML	2020	P. W. Koh, T, Nguyen, and Y. S. Tang et al.	Concept Bottleneck Models	Stanford Univ. and Google Research	[summary]
ICML	2019	S. Kornblith et al.	Similarity of Neural Network Representations Revisited	Google Brain	[official code]

XAI (Explainable AI)

From	Year	Authors	Paper	Institution	url
Arxiv	2019	M. Yang et al.	Benchmarking Attribution Methods with Relative Feature Importance	Google Brain	[official code]
NeurIPS	2019	S. Hooker et al.	A Benchmark for Interpretability Methods in Deep Neural Networks	Google Brain	[summary] [ppt]
ICCV Workshop	2019	B. Kim et al.	Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps	KAIST	[official code]
NeurIPS	2018	J. Adebayo et al.	Sanity Checks for Saliency Maps	Google Brain	-
ICML Workshop	2018	J. Seo et al.	Noise-adding Methods of Saliency Map as Series of Higher Order Partial Derivative	Satrec Initiative, KAIST	-
CVPR	2018	Q. Zhang et al.	Interpretable Convolutional Neural Networks	University of California	[official code]
ICCV	2017	R. Selvaraju et al.	Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization	Georgia Institute of Technology	[official code] [ppt]
CVPR	2017	D. Smilkov et al.	SmoothGrad: removing noise by adding noise	Google Inc.	-

Registration for medical image

From	Year	Authors	Paper	Institution	url
ICCV	2019	S. Zhao et al.	Recursive Cascaded Networks for Unsupervised Medical Image Registration	Tsinghua Univ., Beihang Univ., and Microsoft Research	[official code]
Journal of Biomedical and Health Informatics	2019	S. Zhao et al.	Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Network	Tsinghua Univ., Beihang Univ., and Microsoft Research	[official code] [summary]
CVPR, TMI	2018	G. Balakrishman et al.	VoxelMorph: A Learning Framework for Deformable Medical Image Registration	MIT and Cornell Univ.	[official code] [summary]

Weakly-supervised learning

From	Year	Authors	Paper	Institution	url
CVPR	2019	J. Lee et al.	FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference	Seoul National Univ.	[summary] [ppt]

Representation learning

From	Year	Authors	Paper	Institution	url
CVPR	2019	J. Deng et al.	ArcFace: Additive Angular Margin Loss for Deep Face Recognition	Imperial College London, InsightFace, FaceSoft	[official code] [summary]
CVPR	2017	W. Liu et al.	SphereFace: Deep Hypersphere Embedding for Face Recognition	Georgia Institute of Technology, Carnegie Mellon Univ., and Sun Yat-Sen Univ.	[official code] [summary]

Attention

From	Year	Authors	Paper	Institution	url
CVPR	2020	I. Kim and W. Baek et al.	Spatially Attentive Output Layer for Image Classification	Kakao Brain	[summary]
ECCV	2020	M. KIm and J. Park et al.	Learning Visual Context by Comparison	Lunit Inc. and Seoul National Univ. Hospital	[official code] [summary]
CVPR	2018	J. Hu et al.	Squeeze-and-Excitation Networks	University of Chinese Academy of Sciences	[official code]
ECCV	2018	S. Woo et al.	CBAM: Convolutional Block Attention Module	KAIST	[official code] [summary]

Object Segmentation

From	Year	Authors	Paper	Institution	url
CVPR	2019	A. Kirillov et al.	Panoptic Segmentation	Facebook AI Research (FAIR) and Heidelberg Univ.	[ppt]
ECCV	2018	L. Chen et al.	Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation	Google Inc.	[official code] [ppt]
MIDL	2018	O. Oktay et al.	Attention U-Net: Learning Where to Look for the Pancreas	Imperial College London, Babylon Heath	[official code]
MICCAI	2016	Ö. Çiçek et al.	3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation	University of Freiburg	-
MICCAI	2015	Ö. Ronneberger et al.	U-Net: Convolutional Networks for Biomedical Image Segmentation	University of Freiburg	-
CVPR	2015	J. Long et al.	Fully Convolutional Networks for Semantic Segmentation	UC Berkeley	[official code] [summary]
ICLR	2015	L. Chen et al.	Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs	University of California, Google Inc., and CentraleSupelec	[official code] [summary] [ppt]

Object Detection

From	Year	Authors	Paper	Institution	url
CVPR	2020	G. Song et al.	Revisiting the Sibling Head in Object Detector	SenseTime X-Lab and The Chinese Univ. of Hong Kong	[official code] [summary]
ECCV	2020	N. Carion et al.	End-to-End Object Detection with Transformers	Facebook AI Research (FAIR)	[official code] [custom code] [summary]
CVPR	2019	H. Rezatofighi et al.	Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression	Stanford Univ., The University of Adelaide, and Aibee Inc	[ppt]

Classification

From	Year	Authors	Paper	Institution	url
ICML	2019	M. Tan et al.	EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks	Google Research	[official code] [ppt]
CVPR	2017	S. Xie et al.	Aggregated Residual Transformations for Deep Neural Networks	UC San Diego	[official code]
CVPR	2017	F. Chollet et al.	Xception: Deep Learning with Depthwise Separable Convolutions	Google Inc.	-
CVPR	2016	K. He et al.	Deep Residual Learning for Image Recognition	Microsoft Research	[official code] summary
ICLR	2015	k. Simonyan et al.	Very Deep Convolutional Networks for Large-Scale Image Recognition	University of Oxford	-
CVPR	2015	C. Szegedy et al.	Going Deeper with Convolutions	Google Inc.	[ppt]