InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

Article Details
Title:	InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
Article URLs:	http://papers.nips.cc/paper/6399-infogan-interpretable-representation-learning-by-information-maximizing-generative-adversarial-nets
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Authors:	Xi Chen	UC Berkeley, Department of Electrical Engineering and Computer Sciences OpenAI
	Yan Duan	UC Berkeley, Department of Electrical Engineering and Computer Sciences OpenAI
	Rein Houthooft	UC Berkeley, Department of Electrical Engineering and Computer Sciences OpenAI
	John Schulman	UC Berkeley, Department of Electrical Engineering and Computer Sciences OpenAI
	Ilya Sutskever	Open AI
	Pieter Abbeel	UC Berkeley, Department of Electrical Engineering and Computer Sciences OpenAI
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Artifact Evaluation Badge:	none
Artifact URLs:	https://arxiv.org/abs/1606.03657 http://papers.nips.cc/paper/6399-infogan-interpretable-representation-learning-by-information-maximizing-generative-adversarial-nets-supplemental.zip
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DBLP Key:	conf/nips/ChenCDHSSA16
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Advances in Neural Information Processing Systems, NIPS 2016