关键词: 生成对抗网络（GANs）, 散度函数, 神经网络, 生成模型

Abstract:

Since the birth of generative adversarial networks (GANs), the research on it has become a hot spot in the field of machine learning. It uses the mechanism of adversarial learning to train model solving the problem that the generation algorithm cannot solve. Due to the advantages of GANs, researchers conduct in-depth research on it and  a large number of derivative models of GANs are produced, which empowers the rapid development of GANs and the formation of so-called GAN-Zoo. GANs is widely used in visual field, audio field, natural language field and other fields, such as image generation, image translation, text generation, audio conversion, natural language translation and so on. Based on the traditional GANs, this paper introduces and summarizes the prominent aspects of GANs research in recent years. Firstly, this paper introduces the basic theory of GANs,  summarizes the main derivative models of GANs in recent years, and finally summarizes the main application results of GANs in image field and information security field.

Key words: generative adversarial networks (GANs), divergence function, neural networks, generative model