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InfoGAN: Interpretable Representation Learning by ...
Xi Chen yz, Yan Duan yz, Rein Houthooft yz, John Schulman yz, Ilya Sutskever z, Pieter Abbeel yz y UC Berkeley, Department of Electrical Engineering and Computer Sciences z OpenAI Abstract This paper describes InfoGAN, an information-theoretic extension to the Gener-ative Adversarial Network that is able to learn disentangled representations in a
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