# InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

Xi ChenYan DuanRein HouthooftJohn SchulmanIlya SutskeverPieter Abbeel

This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner. InfoGAN is a generative adversarial network that also maximizes the mutual information between a small subset of the latent variables and the observation... (read more)

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