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Disentangled representations, where the higher level data generative factors are reflected in disjoint latent dimensions, offer several benefits such as ease of deriving invariant representations, transferability to other tasks, interpretability, etc.
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Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Taco Cohen and Max Welling · 2014
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Samuel Gershman and Noah Goodman · 2014
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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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Scott Reed, Kihyuk Sohn, Yuting Zhang, and Honglak Lee · 2014
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Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Tim Van Erven and Peter Harremos · 2014
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Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew M Dai, Rafal Jozefowicz, and Samy Bengio · 2015
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Taco S Cohen and Max Welling · 2015
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Learning to linearize under uncertainty
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