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In contrastive representation learning, data representation is trained so that it can classify the image instances even when the images are altered by augmentations.
Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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Bounds on the jensen gap, and implications for mean concentrated distributions
Xiang Gao, Meera Sithram, and Ardian E. Roitberg · 2016
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Reinforcement learning with deep energy-based policies
Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, and Sergey Levine · 2017
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Learning discrete representations via information maximizing self-augmented training
Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, and Masashi Sugiyama · 2017
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Virtual adversarial training: A regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin ichi Maeda, Masanori Koyama, and Shin Ishii · 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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Autoaugment: Learning augmentation policies from data
Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V. Le · 2019
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Probability: Theory and Examples
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Jonas Rothfuss, Fabio Ferreira, Simon Boehm, Simon Walther, and Andreas Krause Maxim Ulrich, Tamim Asfour · 2019
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Yonglong Tian, Dilip Krishna, and Phillip Isola · 2019
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Faster autoaugment: Learning augmentation strategies using backpropagation
What makes for good views for contrastive learning?
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, and Philip Isola Cordelia Schmid · 2020
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On mutual information maximization for representation learning
Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, and Mario Lucic · 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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On mutual information in contrastive learning for visual representations
Mike Wu, Chengxu Zhuang, Milan Mosse, Daniel Yamins, and Noah Goodman · 2020
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Selfaugment: Automatic augmentation policies for self-supervised learning
Colorado J Reed, Sean Metzger, Aravind Srinivas, Trevor Darrell, and Kurt Keutzer · 2021
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Ryuichiro Hataya, Jan Zdenek, Kazuki Yoshizoe, and Hideki Nakayama · 2020
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Data-efficient image recognition with contrastive predictive coding
Olivier J. Hénaff, Aravind Srinivas, Jeffrey De Fauw, Ali Razavi, Carl Doersch, S. M. Ali Eslami, and Aaron van den Oord · 2020
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Viewmaker networks: Learning views for unsupervised representation learning
Alex Tamkin, Mike Wu, and Noah Goodman · 2021
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Self-supervised learning with data augmentations provably isolates content from style
Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, and Francesco Locatello · 2021
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