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We propose an approach to self-supervised representation learning based on maximizing mutual information between features extracted from multiple views of a shared context.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann and Aapo Hyvärinen · 2010
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An analysis of single layer networks in unsupervised feature learning
Adam Coates, Honglak Lee, and Andrew Y Ng · 2011
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
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Decaf: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell · 2014
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Discriminative unsupervised feature learning with exemplar convolutional neural networks
Alexey Dosovitskiy, Philipp Fischer, Jost Tobias Springenberg, and Thomas Brox Martin Riedmiller · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning · 2014
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Learning deep features for scene recognition using places database
Bolei Zhou, Agata Lapedriza, Jianxiong Xiao, Antonio Torralba, and Audo Oliva · 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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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 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, Alexander C Berg, and Li Fei-Fei · 2015
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Training very deep networks
Rupesh Kumar Srivastava, Klaus Greff, and J urgen Schmidhuber · 2015
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
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Look, listen and learn
Relja Arandjelović and Andrew Zisserman · 2017
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Quo vadis, action recognition? a new model and the kinetics dataset
Joao Carreira and Andrew Zisserman · 2017
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Multi-task self-supervised visual learning
Carl Doersch and Andrew Zisserman · 2017
Cited alongside, same era.
Reinforcement learning with deep energy-based policies
Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
Cited alongside, same era.
Time-contrastive networks: Self-supervised learning from video
Pierre Sermanet, Corey Lynch, Yevgen Chebotar, Jasmine Hsu, Eric Jang, Stefan Schaal, and Sergey Levine · 2017
Cited alongside, same era.
Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
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Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Tracking emerges by colorizing videos
Carl Vondrick, Abhinav Shrivastava, Alireza Fathi, Sergio Guadarrama, and Kevin Murphy · 2018
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Breaking the softmax bottleneck: A high-rank rnn language model
Zhilin Yang, Zihang Dai, Ruslan Salakhutdinov, and William W Cohen · 2018
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Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel · 2019
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Objects that sound
Relja Arandjelović and Andrew Zisserman · 2018
Cited alongside, same era.
Learning actionable representations from visual observations
Debidatta Dwibedi, Jonathan Tompson, Corey Lynch, and Pierre Sermanet · 2018
Cited alongside, same era.
Neural scene representation and rendering
SM Ali Eslami, Danilo Jimenez Rezende, Frederic Besse, Fabio Viola, Aria S Morcos, Marta Garnelo, Avraham Ruderman, Andrei A Rusu, Ivo Danihelka, Karol Gregor, David P Reichert, Lars Buesing, Theophane Weber, Oriol Vinyals, Dan Rosenbaum, Neil Rabinowitz, Helen King, Chloe Hillier, Matt Botvinick, Daan Wierstra, Koray Kavukcuoglu, and Demis Hassabis · 2018
Cited alongside, same era.
Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
Cited alongside, same era.
Rethinking imagenet pre-trainining
Kaiming He, Ross Girshick, and Piotr Dollár · 2018
Cited alongside, same era.
An efficient framework for learning sentence representations
Lajanugen Logeswaran and Honglak Lee · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Scaling and benchmarking self-supervised visual representation learning
Priya Goyal, Dhruv Mahajan, Abhinav Gupta, and Ishan Misra · 2019
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Data-efficient image recognition with contrastive predictive coding
Olivier J Hénaff, Ali Rezavi, Carl Doersch, S M Ali Eslami, and Aaron van den Oord · 2019
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Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Philip Bachman, Adam Trischler, and Yoshua Bengio · 2019
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Invariant information clustering for unsupervised image classification and segmentation
Xu Ji, Jo ao F Henriques, and Andrea Vedaldi · 2019
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Revisiting self-supervised learning
Alexander Kolesnikov, Xiaohua Zhai, and Lucas Beyer · 2019
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Sungbim Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, and Sungwoong Kim · 2019
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On variational bounds of mutual information
Ben Poole, Sherjil Ozair, Aaron van den Oord, Alexander A Alemi, and George Tucker · 2019
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Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
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