自动化学报2017，Vol.43Issue(3)：321-332,12.DOI:10.16383/j.aas.2017.y000003
生成式对抗网络GAN的研究进展与展望
Generative Adversarial Networks: The State of the Art and Beyond
摘要
Abstract
Generative adversarial networks (GANs) have become a hot research topic in artificial intelligence. Inspired by the two-player zero-sum game, GAN is composed of a generator and a discriminator, both trained with the adversarial learning mechanism. The aim of GAN is to estimate the potential distribution of existing data and generate new data samples from the same distribution. Since its initiation, GAN has been widely studied due to its enormous prospect for applications, including image and vision computing, speech and language processing, information security, and chess game. In this paper we summarize the state of the art of GAN and look into its future. First of all, we survey the GAN's background, theoretic and implementation models, application fields, advantages and disadvantages, and development trends. Then, we investigate the relation between GAN and parallel intelligence with the conclusion that GAN has a great potential in parallel systems especially in computational experiments, in terms of virtual-real interaction and integration. Finally, we clarify that GAN can provide specific and substantial algorithmic support for the ACP theory.关键词
生成式对抗网络/生成式模型/零和博弈/对抗学习/平行智能/ACP方法Key words
Generative adversarial networks/generative models/zero-sum game/adversarial learning/parallel intelli-gence/ACP methodology引用本文复制引用
王坤峰,苟超,段艳杰,林懿伦,郑心湖,王飞跃..生成式对抗网络GAN的研究进展与展望[J].自动化学报,2017,43(3):321-332,12.基金项目
国家自然科学基金(61533019,71232006,91520301)资助Supported by National Natural Science Foundation of China(61533019,71232006,91520301) （61533019,71232006,91520301）
