InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

NeurIPS 2016 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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Evaluation results from the paper


Task Dataset Model Metric name Metric value Global rank Compare
Unsupervised MNIST MNIST InfoGAN Accuracy 95 # 5
Unsupervised image classification MNIST InfoGAN Accuracy 95 # 5