Architecture and algorithm development for generative adversarial networks - Yazici, Yasin (Nanyang Technological University, 2020)
Generative Adversarial Networks (GANs) are recently invented generative models which can produce highly realistic samples such as images. In GANs framework, a generator and a discriminator play a game against each other. The discriminator aims to find differences between the data distribution and the generator's distribution, while the generator aims to trick the discriminator. Both players update their own parameters, in an iterative manner, to satisfy their own objective. By playing this game, the generator learns to produce samples that are similar to the ones in the data distribution.