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In a wide range of multimodal tasks, contrastive learning has become a particularly appealing approach since it can successfully learn representations from abundant unlabeled data with only pairing information (e.g., image-caption or video-audio pairs).
Understanding the limitations of variational mutual information estimators
Jiaming Song and Stefano Ermon · 1910
Earlier work this paper cites.
A mathematical theory of communication
Claude Elwood Shannon · 1948
Earlier work this paper cites.
Multivariate information transmission
William McGill · 1954
Earlier work this paper cites.
Information theory and statistics
Thomas M Cover and Joy A Thomas · 1991
Earlier work this paper cites.
Relations between entropy and error probability
Meir Feder and Neri Merhav · 1994
Earlier work this paper cites.
The co-information lattice
Anthony J Bell · 2003
Earlier work this paper cites.
A taxonomy of relationships between images and text
Emily E Marsh and Marilyn Domas White · 2003
Earlier work this paper cites.
An information theoretic framework for multi-view learning
Karthik Sridharan and Sham M Kakade · 2008
Earlier work this paper cites.
Estimating divergence functionals and the likelihood ratio by convex risk minimization
XuanLong Nguyen, Martin J Wainwright, and Michael I Jordan · 2010
Earlier work this paper cites.
Nonnegative decomposition of multivariate information
Paul L Williams and Randall D Beer · 2010
Earlier work this paper cites.
The mnist database of handwritten digit images for machine learning research
Li Deng · 2012
Earlier work this paper cites.
Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
Earlier work this paper cites.
A review of feature selection methods based on mutual information
Jorge R Vergara and Pablo A Estévez · 2014
Earlier work this paper cites.
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Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai · 2016
Earlier work this paper cites.
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Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2016
Earlier work this paper cites.
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Alistair EW Johnson, Tom J Pollard, Lu Shen, Li-wei H Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark · 2016
Earlier work this paper cites.
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Earlier work this paper cites.
Look, listen and learn
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Earlier work this paper cites.
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Earlier work this paper cites.
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R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2018
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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AmirAli Bagher Zadeh, Paul Pu Liang, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency · 2018
Earlier work this paper cites.
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Philip Bachman, R Devon Hjelm, and William Buchwalter · 2019
Earlier work this paper cites.
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Earlier work this paper cites.
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Md Kamrul Hasan, Wasifur Rahman, AmirAli Bagher Zadeh, Jianyuan Zhong, Md Iftekhar Tanveer, Louis-Philippe Morency, and Mohammed Ehsan Hoque · 2019
Earlier work this paper cites.
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Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 2019
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Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee · 2019
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Wasserstein dependency measure for representation learning
Sherjil Ozair, Corey Lynch, Yoshua Bengio, Aaron Van den Oord, Sergey Levine, and Pierre Sermanet · 2019
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Cited alongside, same era.
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Tianyu Gao, Xingcheng Yao, and Danqi Chen · 2021
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Mural: multimodal, multitask retrieval across languages
Aashi Jain, Mandy Guo, Krishna Srinivasan, Ting Chen, Sneha Kudugunta, Chao Jia, Yinfei Yang, and Jason Baldridge · 2021
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Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
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