How Generative Adversarial Networks and its variants Work: An Overview of GAN
Yongjun Hong, Uiwon Hwang, Jaeyoon Yoo, Sungroh Yoon · arXiv (Cornell University) · 2017
Generative adversarial networks (GANs) have received wide attention in the machine learning field because of their potential to learn high-dimensional, complex real data. Specifically, they do not perform distribution assumptions and can simply infer real-like samples from latent space. This powerful property leads GANs to be applied to various applications such as image synthesis, image attribute editing, image translation, domain adaptation and other academic fields. In this review, we aim to discuss details of GANs for those readers who are familiar but do not comprehend GANs deeply, or who wish to evaluate GANs from various perspectives. We discuss how a GAN operates and the fundamental meaning of various objective functions suggested recently. We then focus on how the GAN can be combined with an auto-encoder framework which makes it possible handle the latent space. As an extension, we also discuss GAN variants that are applied to various tasks and other fields.