Overview of Generative Adversarial Networks

Abstract

The study of generative adversarial networks (GAN) provides a new approach and framework for computer vision and makes an outstanding choice for cross-domain image translation problems. In this work, the basic framework structure of GAN as well as the evolution of GAN are summarized. It is known that the unsupervised domain adaptation algorithms attempt to map representations between the two domains, or learn to extract features that are domain–invariant. An approach that learns in an unsupervised manner can be a transformation in the pixel space from one domain to the other. Finally, some existing problems of GAN and its potential future are also summarized and discussed.