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Multiview data contain information from multiple modalities and have potentials to provide more comprehensive features for diverse machine learning tasks.
The information bottleneck method
Naftali Tishby, Fernando C Pereira, and William Bialek · 2000
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Multimodal deep learning
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, and Andrew Y. Ng · 2011
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The mnist database of handwritten digit images for machine learning research [best of the web]
Li Deng · 2012
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A survey on multi-view learning
Chang Xu, Dacheng Tao, and Chao Xu · 2013
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Deep canonical correlation analysis
Galen Andrew, Raman Arora, Jeff Bilmes, and Karen Livescu · 2013
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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On deep multi-view representation learning
Weiran Wang, Raman Arora, Karen Livescu, and Jeff Bilmes · 2015
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Deep learning and the information bottleneck principle
Naftali Tishby and Noga Zaslavsky · 2015
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On deep multi-view representation learning
Weiran Wang, Raman Arora, Karen Livescu, and Jeff Bilmes · 2015
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Deep variational canonical correlation analysis
Weiran Wang, Xinchen Yan, Honglak Lee, and Karen Livescu · 2016
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Deep and structured robust information theoretic learning for image analysis
Yue Deng, Feng Bao, Xuesong Deng, Ruiping Wang, Youyong Kong, and Qionghai Dai · 2016
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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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Deep variational information bottleneck
Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy · 2017
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Mine: mutual information neural estimation
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and R Devon Hjelm · 2018
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Data-dependence of plateau phenomenon in learning with neural network—statistical mechanical analysis
Yuki Yoshida and Masato Okada · 2019
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Learning representations for neural network-based classification using the information bottleneck principle
Rana Ali Amjad and Bernhard C Geiger · 2019
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On variational bounds of mutual information
Ben Poole, Sherjil Ozair, Aaron Van Den Oord, Alex Alemi, and George Tucker · 2019
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Gait recognition via disentangled representation learning
Ziyuan Zhang, Luan Tran, Xi Yin, Yousef Atoum, Xiaoming Liu, Jian Wan, and Nanxin Wang · 2019
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Luan Tran, Xi Yin, and Xiaoming Liu · 2017
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A survey of multi-view representation learning
Yingming Li, Ming Yang, and Zhongfei Zhang · 2018
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Mutual information neural estimation
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and Devon Hjelm · 2018
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Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2018
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Understanding disentangling in b e t a beta -vae
Christopher P Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner · 2018
Cited alongside, same era.
Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos, Evgenia Rusak, Oliver Bringmann, Alexander S. Ecker, Matthias Bethge, and Wieland Brendel · 2019
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Lucas Beyer, Olivier J Hénaff, Alexander Kolesnikov, Xiaohua Zhai, and Aäron van den Oord · 2020
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From imagenet to image classification: Contextualizing progress on benchmarks
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Andrew Ilyas, and Aleksander Madry · 2020
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Learning robust representations via multi-view information bottleneck
Marco Federici, Anjan Dutta, Patrick Forré, Nate Kushman, and Zeynep Akata · 2020
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