A Review: Generative Adversarial Networks

Abstract

Deep learning has achieved great success in the field of artificial intelligence, and many deep learning models have been developed. Generative Adversarial Networks (GAN) is one of the deep learning model, which was proposed based on zero-sum game theory and has become a new research hotspot. The significance of the model variation is to obtain the data distribution through unsupervised learning and to generate more realistic/actual data. Currently, GANs have been widely studied due to the enormous application prospect, including image and vision computing, video and language processing, etc. In this paper, the background of the GAN, theoretic models and extensional variants of GANs are introduced, where the variants can further optimize the original GAN or change the basic structures. Then the typical applications of GANs are explained. Finally the existing problems of GANs are summarized and the future work of GANs models are given.