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Self-supervised learning has emerged as a strategy to reduce the reliance on costly supervised signal by pretraining representations only using unlabeled data.
On the evolution of random graphs
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A simple framework for contrastive learning of visual representations
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Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
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The elements of statistical learning , volume 2
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann and Aapo Hyvärinen · 2010
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Rectified linear units improve restricted boltzmann machines
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The arcade learning environment: An evaluation platform for general agents (extended abstract)
Marc G. Bellemare, Yavar Naddaf, J. Veness, and Michael Bowling · 2013
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Visual causal feature learning
Krzysztof Chalupka, Pietro Perona, and Frederick Eberhardt · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and Christian Szegedy · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Zhiheng Huang, A. Karpathy, A. Khosla, M. Bernstein, A. Berg, and Li Fei-Fei · 2015
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Introduction to random graphs
Alan Frieze and Michał Karoński · 2016
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Domain adaptation with conditional transferable components
Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, and Bernhard Schölkopf · 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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Causal inference by using invariant prediction: identification and confidence intervals
Jonas Peters, Peter Bühlmann, and Nicolai Meinshausen · 2016
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Conditional variance penalties and domain shift robustness
Christina Heinze-Deml and Nicolai Meinshausen · 2017
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Sgdr: Stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter · 2017
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Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
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Revisiting self-supervised visual representation learning
A. Kolesnikov, Xiaohua Zhai, and Lucas Beyer · 2019
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On variational bounds of mutual information
Ben Poole, Sherjil Ozair, Aaron Van Den Oord, Alex Alemi, and George Tucker · 2019
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A theoretical analysis of contrastive unsupervised representation learning
Nikunj Saunshi, Orestis Plevrakis, Sanjeev Arora, Mikhail Khodak, and Hrishikesh Khandeparkar · 2019
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Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
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On mutual information maximization for representation learning
Michael Tschannen, Josip Djolonga, Paul K Rubenstein, Sylvain Gelly, and Mario Lucic · 2019
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Yang You, Igor Gitman, and Boris Ginsburg · 2017
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Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Learning representations by maximizing mutual information across views
Philip Bachman, R Devon Hjelm, and William Buchwalter · 2019
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2019
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Data-efficient image recognition with contrastive predictive coding
Olivier J Hénaff, Ali Razavi, Carl Doersch, SM Eslami, and Aaron van den Oord · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Haohan Wang, Songwei Ge, E. Xing, and Zachary Chase Lipton · 2019
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Unsupervised learning of visual features by contrasting cluster assignments
M. Caron, I. Misra, J. Mairal, Priya Goyal, P. Bojanowski, and Armand Joulin · 2020
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Bootstrap your own latent: A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, et al · 2020
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The many faces of robustness: A critical analysis of out-of-distribution generalization
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Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
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Self-supervised learning of pretext-invariant representations
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Curl: Contrastive unsupervised representations for reinforcement learning
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What makes for good views for contrastive learning
Yonglong Tian, C. Sun, Ben Poole, Dilip Krishnan, C. Schmid, and Phillip Isola · 2020
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