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In the past few years, contrastive learning has played a central role for the success of visual unsupervised representation learning.
Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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One weird trick for parallelizing convolutional neural networks
Alex Krizhevsky · 2014
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Deep metric learning via lifted structured feature embedding
Hyun Oh Song, Yu Xiang, Stefanie Jegelka, and Silvio Savarese · 2016
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Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros · 2016
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Accurate, large minibatch sgd: Training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
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Smart mining for deep metric learning
Ben Harwood, Vijay Kumar BG, Gustavo Carneiro, Ian Reid, and Tom Drummond · 2017
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Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, et al · 2017
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Cyclical learning rates for training neural networks
Leslie N Smith · 2017
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Sampling matters in deep embedding learning
Chao-Yuan Wu, R Manmatha, Alexander J Smola, and Philipp Krahenbuhl · 2017
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Large batch training of convolutional networks
Yang You, Igor Gitman, and Boris Ginsburg · 2017
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
Cited alongside, same era.
Mining on manifolds: Metric learning without labels
Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondřej Chum · 2018
Cited alongside, same era.
i-revnet: Deep invertible networks
Jörn-Henrik Jacobsen, Arnold Smeulders, and Edouard Oyallon · 2018
Cited alongside, same era.
Revisiting small batch training for deep neural networks
Dominic Masters and Carlo Luschi · 2018
Cited alongside, same era.
Group normalization
Yuxin Wu and Kaiming He · 2018
Cited alongside, same era.
Pytorch distributed: Experiences on accelerating data parallel training
Shen Li, Yanli Zhao, Rohan Varma, Omkar Salpekar, Pieter Noordhuis, Teng Li, Adam Paszke, Jeff Smith, Brian Vaughan, Pritam Damania, et al · 2020
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Self-supervised learning of pretext-invariant representations
Ishan Misra and Laurens van der Maaten · 2020
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Contrastive learning with hard negative samples
Joshua Robinson, Ching-Yao Chuang, Suvrit Sra, and Stefanie Jegelka · 2020
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Contrastive multiview coding
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2020
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Tongzhou Wang and Phillip Isola · 2020
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Hang Zhang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, Ambrish Tyagi, and Amit Agrawal · 2018
Cited alongside, same era.
Deepusps: Deep robust unsupervised saliency prediction via self-supervision
Tam Nguyen, Maximilian Dax, Chaithanya Kumar Mummadi, Nhung Ngo, Thi Hoai Phuong Nguyen, Zhongyu Lou, and Thomas Brox · 2019
Cited alongside, same era.
Super-convergence: Very fast training of neural networks using large learning rates
Leslie N Smith and Nicholay Topin · 2019
Cited alongside, same era.
Stochastic class-based hard example mining for deep metric learning
Yumin Suh, Bohyung Han, Wonsik Kim, and Kyoung Mu Lee · 2019
Cited alongside, same era.
Fixing the train-test resolution discrepancy
Hugo Touvron, Andrea Vedaldi, Matthijs Douze, and Hervé Jégou · 2019
Cited alongside, same era.
On mutual information maximization for representation learning
Michael Tschannen, Josip Djolonga, Paul K Rubenstein, Sylvain Gelly, and Mario Lucic · 2019
Cited alongside, same era.
Hardness-aware deep metric learning
Wenzhao Zheng, Zhaodong Chen, Jiwen Lu, and Jie Zhou · 2019
Cited alongside, same era.
Mike Wu, Milan Mosse, Chengxu Zhuang, Daniel Yamins, and Noah Goodman · 2020
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What should not be contrastive in contrastive learning
Tete Xiao, Xiaolong Wang, Alexei A Efros, and Trevor Darrell · 2020
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Hard negative examples are hard, but useful
Hong Xuan, Abby Stylianou, Xiaotong Liu, and Robert Pless · 2020
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Vicreg: Variance-invariance-covariance regularization for self-supervised learning
Adrien Bardes, Jean Ponce, and Yann LeCun · 2021
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Intriguing properties of contrastive losses
Ting Chen, Calvin Luo, and Lala Li · 2021
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Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
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On feature decorrelation in self-supervised learning
Tianyu Hua, Wenxiao Wang, Zihui Xue, Sucheng Ren, Yue Wang, and Hang Zhao · 2021
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Understanding the behaviour of contrastive loss
Feng Wang and Huaping Liu · 2021
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Barlow twins: Self-supervised learning via redundancy reduction
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny · 2021
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Contrastive attraction and contrastive repulsion for representation learning
Huangjie Zheng, Xu Chen, Jiangchao Yao, Hongxia Yang, Chunyuan Li, Ya Zhang, Hao Zhang, Ivor Tsang, Jingren Zhou, and Mingyuan Zhou · 2021
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Boosting contrastive self-supervised learning with false negative cancellation
Tri Huynh, Simon Kornblith, Matthew R Walter, Michael Maire, and Maryam Khademi · 2022
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Nenad Tomasev, Ioana Bica, Brian McWilliams, Lars Buesing, Razvan Pascanu, Charles Blundell, and Jovana Mitrovic · 2022
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