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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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK BENCHMARK
Unsupervised MNIST MNIST InfoGAN Accuracy 95 # 6
Unsupervised Image Classification MNIST InfoGAN Accuracy 95 # 6

Results from Other Papers


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK SOURCE PAPER COMPARE
Image Generation CUB 128 x 128 InfoGAN FID 13.20 # 2
Inception score 47.32 # 2
Image Generation Stanford Cars InfoGAN FID 17.63 # 2
Inception score 28.62 # 2
Image Generation Stanford Dogs InfoGAN FID 29.34 # 2
Inception score 43.16 # 2

Methods used in the Paper