An Overview of Generative Adversarial Networks

Abstract

Generative adversarial network (GAN), put forward by two-person zero-sum game theory, is one of the most important research hotspots in the field of artificial intelligence. With a generator network and a discriminator network, GAN is trained by adversarial learning. In this paper, we aim to discusses the development status of GAN. We first introduce the basic idea and training process of GAN in detail, and summarize the structure and structure of GAN derivative models, including conditional GAN, deep convolution DCGAN, WGAN based on Wasserstein distance and WGAN-GP based on gradient strategy. We also introduce the specific applications of GAN in the fields of information security, face recognition, 3D and video technology, and summarize the shortcomings of GAN. Finally, we look forward to the development trend of GAN.