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Self-supervised learning (SSL) is currently one of the premier techniques to create data representations that are actionable for transfer learning in the absence of human annotations.
Intrinsic dimension of data representations in deep neural networks
Alessio Ansuini, Alessandro Laio, Jakob H. Macke, and Davide Zoccolan · 1905
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Self-supervised learning of pretext-invariant representations
Ishan Misra and Laurens van der Maaten · 1912
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A global geometric framework for nonlinear dimensionality reduction
Joshua B Tenenbaum, Vin De Silva, and John C Langford · 2000
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton · 2002
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Non-local manifold tangent learning
Yoshua Bengio and Martin Monperrus · 2005
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Non-local manifold parzen windows
Yoshua Bengio, Hugo Larochelle, and Pascal Vincent · 2005
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Prototypical contrastive learning of unsupervised representations
Junnan Li, Pan Zhou, Caiming Xiong, Richard Socher, and Steven C. H. Hoi · 2005
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What makes for good views for contrastive learning
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2005
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Tongzhou Wang and Phillip Isola · 2005
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2006
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Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey Hinton · 2006
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Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey E. Hinton · 2006
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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 Ávila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko · 2006
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Provable finite data generalization with group autoencoder
Romain Cosentino, Randall Balestriero, Richard G. Baraniuk, and Behnaam Aazhang · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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How well do self-supervised models transfer?
Linus Ericsson, Henry Gouk, and Timothy M. Hospedales · 2011
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Lie Groups, Lie Algebras, and Representations: an Elementary Introduction , volume 222
B. Hall · 2015
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Unsupervised transformation learning via convex relaxations
Tatsunori B Hashimoto, Percy S Liang, and John C Duchi · 2017
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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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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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Variational autoencoder with learned latent structure
Marissa Connor, Gregory Canal, and Christopher Rozell · 2021
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Provable guarantees for self-supervised deep learning with spectral contrastive loss
Jeff Z HaoChen, Colin Wei, Adrien Gaidon, and Tengyu Ma · 2021
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Towards the generalization of contrastive self-supervised learning
Weiran Huang, Mingyang Yi, and Xuyang Zhao · 2021
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Yang You, Igor Gitman, and Boris Ginsburg · 2017
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A spline theory of deep learning
Randall Balestriero et al · 2018
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Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
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The geometry of deep networks: Power diagram subdivision
Randall Balestriero, Romain Cosentino, Behnaam Aazhang, and Richard Baraniuk · 2019
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Representation learning with contrastive predictive coding, 2019
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2019
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Towards good practices in self-supervised representation learning, 2020
Srikar Appalaraju, Yi Zhu, Yusheng Xie, and István Fehérvári · 2020
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Batch normalization provably avoids ranks collapse for randomly initialised deep networks
Hadi Daneshmand, Jonas Kohler, Francis Bach, Thomas Hofmann, and Aurelien Lucchi · 2020
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Understanding dimensional collapse in contrastive self-supervised learning
Li Jing, Pascal Vincent, Yann LeCun, and Yuandong Tian · 2021
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Sharp bounds for the number of regions of maxout networks and vertices of minkowski sums, 2021
Guido Montúfar, Yue Ren, and Leon Zhang · 2021
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Understanding self-supervised learning dynamics without contrastive pairs
Yuandong Tian, Xinlei Chen, and Surya Ganguli · 2021
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Understanding the behaviour of contrastive loss
Feng Wang and Huaping Liu · 2021
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Decoupled contrastive learning
Chun-Hsiao Yeh, Cheng-Yao Hong, Yen-Chi Hsu, Tyng-Luh Liu, Yubei Chen, and Yann LeCun · 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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Deep autoencoders: From understanding to generalization guarantees
Romain Cosentino, Randall Balestriero, Richard Baranuik, and Behnaam Aazhang · 2022
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