Content
This post is about my understanding of generative adversarial network (GAN), including what is GAN, the derivation of basic theory, and GAN's general framework. In addition, the conditional GANs with supervised and unsupervised learning are exhaustively introduced. After that, several representative GAN methods are discussed including, among others, WGAN, EBGAN, InfoGAN, VAEGAN, and BiGAN. Finally, several applications of GAN are mentioned including photo editing and sequence generation, where some basic ideas and derivation of reinforcement learning are provided.
