Jiangsu Engineering Center of Network monitoring, Jiangsu Collaborative Innovation Center on Atmospheric, School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China
Weijie Yu
Jiangsu Engineering Center of Network monitoring, Jiangsu Collaborative Innovation Center on Atmospheric, School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China
Xiaokai Yi
Jiangsu Engineering Center of Network monitoring, Jiangsu Collaborative Innovation Center on Atmospheric, School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China
Asifullah Khan
Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, Pakistan
Feng Yuan
Jiangsu Engineering Center of Network monitoring, Jiangsu Collaborative Innovation Center on Atmospheric, School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China
Jiangsu Engineering Center of Network monitoring, Jiangsu Collaborative Innovation Center on Atmospheric, School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China
Generative adversarial network (GANs) is one of the most important research avenues in the field of artificial intelligence, and its outstanding data generation capacity has received wide attention. In this paper, we present the recent progress on GANs. First, the basic theory of GANs and the differences among different generative models in recent years were analyzed and summarized. Then, the derived models of GANs are classified and introduced one by one. Third, the training tricks and evaluation metrics were given. Fourth, the applications of GANs were introduced. Finally, the problem, we need to address, and future directions were discussed.
