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InfoGAN: Interpretable Representation Learning by ... - NIPS

InfoGAN: Interpretable Representation Learning byInformation Maximizing Generative Adversarial NetsXi Chen , Yan Duan , Rein Houthooft , John Schulman , Ilya Sutskever , Pieter Abbeel UC Berkeley, Department of Electrical Engineering and Computer Sciences OpenAIAbstractThis paper describes InfoGAN, an information-theoretic extension to the Gener-ative Adversarial Network that is able to learn disentangled representations in acompletely unsupervised manner. InfoGAN is a generative adversarial networkthat also maximizes the mutual information between a small subset of the latentvariables and the observation.

supervised learning: bilinear models [18] separate style and content; multi-view perceptron [19] separate face identity and view point; and Yang et al. [20] developed a recurrent variant that generates a sequence of latent factor transformations. Similarly, VAEs [5] and Adversarial Autoencoders [9]

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