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Recent literature in self-supervised has demonstrated significant progress in closing the gap between supervised and unsupervised methods in the image and text domains.
Learning with local and global consistency
Dengyong Zhou, Olivier Bousquet, Thomas N Lal, Jason Weston, and Bernhard Schölkopf · 2004
Earlier work this paper cites.
Semi-supervised learning by entropy minimization
Yves Grandvalet, Yoshua Bengio, et al · 2005
Earlier work this paper cites.
Semi-supervised learning with graphs
Xiaojin Zhu, John Lafferty, and Ronald Rosenfeld · 2005
Earlier work this paper cites.
A scalable hierarchical distributed language model
Andriy Mnih and Geoffrey E Hinton · 2008
Earlier work this paper cites.
Semi-supervised learning (chapelle, o. et al., eds.; 2006)[book reviews]
Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien · 2009
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
Earlier work this paper cites.
Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
Earlier work this paper cites.
Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
Earlier work this paper cites.
Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros · 2016
Earlier work this paper cites.
Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen · 2016
Earlier work this paper cites.
Improved deep metric learning with multi-class n-pair loss objective
Kihyuk Sohn · 2016
Cited alongside, same era.
Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
Cited alongside, same era.
Catboost: unbiased boosting with categorical features
Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin · 2017
Cited alongside, same era.
Antti Tarvainen and Harri Valpola · 2017
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
Cited alongside, same era.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Later among the works it cites.
Tabnet: Attentive interpretable tabular learning
Sercan O Arik and Tomas Pfister · 2019
Later among the works it cites.
Label propagation for deep semi-supervised learning
Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondrej Chum · 2019
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Manifold mixup: Better representations by interpolating hidden states
Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, and Yoshua Bengio · 2019
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A simple framework for contrastive learning of visual representations, 2020
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Later among the works it cites.
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
Low-shot learning with large-scale diffusion
Matthijs Douze, Arthur Szlam, Bharath Hariharan, and Hervé Jégou · 2018
Cited alongside, same era.
Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
Cited alongside, same era.
An efficient framework for learning sentence representations
Lajanugen Logeswaran and Honglak Lee · 2018
Cited alongside, same era.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong hyun Lee
Cited in the paper.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Later among the works it cites.
Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 2020
Later among the works it cites.
Tabert: Pretraining for joint understanding of textual and tabular data
Pengcheng Yin, Graham Neubig, Wen-tau Yih, and Sebastian Riedel · 2020
Later among the works it cites.
Vime: Extending the success of self-and semi-supervised learning to tabular domain
Jinsung Yoon, Yao Zhang, James Jordon, and Mihaela van der Schaar · 2020
Later among the works it cites.