InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

Understand

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.
• We derive a lower bound to the mutual information objective that can be optimized efficiently, and show that our training procedure can be interpreted as a variation of the Wake-Sleep algorithm.
• Specifically, InfoGAN successfully disentangles writing styles from digit shapes on the MNIST dataset, pose from lighting of 3D rendered images, and background digits from the central digit on the SVHN dataset.