Learning Disentangled Joint Continuous and Discrete Representations

Statistical theory of extreme valuse and some practical applications

Emil Julius Gumbel · 1954

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Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013

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Generative adversarial nets

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014

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Learning to disentangle factors of variation with manifold interaction

Scott Reed, Kihyuk Sohn, Yuting Zhang, and Honglak Lee · 2014

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Stochastic backpropagation and approximate inference in deep generative models

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Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014

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Deep learning face attributes in the wild

Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015

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Weakly-supervised disentangling with recurrent transformations for 3d view synthesis

Jimei Yang, Scott E Reed, Ming-Hsuan Yang, and Honglak Lee · 2015

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Infogan: Interpretable representation learning by information maximizing generative adversarial nets

Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 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 · 2016

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Categorical reparameterization with gumbel-softmax

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Eric Jang, Shixiang Gu, and Ben Poole · 2016

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Understanding disentangling in beta-vae

Christopher Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner · 2017

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Improved training of wasserstein gans

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Alireza Makhzani and Brendan J Frey · 2017

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Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017

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Isolating sources of disentanglement in variational autoencoders