Survey of Generative Adversarial Networks.

With its strong adversary learning ability, generative adversarial networks (GAN) is favored by more and more researchers in many fields. This paper expounds the development background, framework and objective function of GAN, analyzes the causes of pattern collapse and gradient disappearance in the training process, and introduces in detail the GAN derived model proposed through the change of architecture and the modification of objective function. Then, it summarizes some standards used to evaluate the quality and diversity of generated images, and summarizes the wide application of GAN in different fields, Finally, this paper summarizes and puts forward some prospects for the future research direction in this field. [ABSTRACT FROM AUTHOR]