Thank you to Kumar Krishna Agrawal, Yasaman Bahri, Peter Chen, Nic Ford, Roy Frostig, Xinyang Geng, Rein Houthooft, Ben Poole, Colin Raffel and Supasorn Suwajanakorn for contributing to this guide. Why InfoGAN is an extension of GANs that learns to represent unlabeled data as codes, aka representation learning. Compare this to vanilla GANs that can only generate samples or to VAEs that learn to bo