Reviews: InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

It is a good paper that should definitely be accepted. The presented approach has a clear theoretical motivation and is supported by a thorough and convincing experimental evaluation. It is important that the approach does not use any domain-specific knowledge and effectively comes at zero additional computational cost. This makes it easily applicable to a wide range of generative tasks. I have several questions/comments: 1) It seems to me that the proposed approach in the end amounts to training a GAN with an additional network (or an additional branch on top of the discriminator) trained to predict part of the latent code from the generated image.