InfoGAN: Interpretable Representation Learning by Information Maximizing\n Generative Adversarial Nets

This paper describes InfoGAN, an information-theoretic extension to the\nGenerative Adversarial Network that is able to learn disentangled\nrepresentations in a completely unsupervised manner. InfoGAN is a generative\nadversarial network that also maximizes the mutual information between a small\nsubset of the latent variables and the observation. We derive a lower bound to\nthe mutual information objective that can be optimized efficiently, and show\nthat our training procedure can be interpreted as a variation of the Wake-Sleep\nalgorithm. Specifically, InfoGAN successfully disentangles writing styles from\ndigit shapes on the MNIST dataset, pose from lighting of 3D rendered images,\nand background digits…