Generative Adversarial Networks: Introduction and Outlook
Kunfeng Wang1,2; Chao Gou1,3; Yanjie Duan1,3; Yilun Lin1,3; Xinhu Zheng4; Fei-Yue Wang1,5
Source PublicationIEEE/CAA Journal of Automatica Sinica

Recently, generative adversarial networks (GANs) have become a research focus of artificial intelligence. Inspired by two-player zero-sum game, GANs comprise a generator and a discriminator, both trained under the adversarial learning idea. The goal of GANs is to estimate the potential distribution of real data samples and generate new samples from that distribution. Since their initiation, GANs have been widely studied due to their enormous prospect for applications, including image and vision computing, speech and language processing, etc. In this review paper, we summarize the state of the art of GANs and look into the future. Firstly, we survey GANs’ proposal background, theoretic and implementation models, and application fields. Then, we discuss GANs’ advantages and disadvantages, and their development trends. In particular, we investigate the relation between GANs and parallel intelligence, with the conclusion that GANs have a great potential in parallel systems research in terms of virtual-real interaction and integration. Clearly, GANs can provide substantial algorithmic support for parallel intelligence.

KeywordAcp Approach Adversarial Learning Generative Adversarial Networks (Gans) Generative Models Parallel Intelligence Zero-sum Game
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Document Type期刊论文
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.Qingdao Academy of Intelligent Industries
3.University of Chinese Academy of Sciences
4.University of Minnesota
5.National University of Defense Technology
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
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GB/T 7714
Kunfeng Wang,Chao Gou,Yanjie Duan,et al. Generative Adversarial Networks: Introduction and Outlook[J]. IEEE/CAA Journal of Automatica Sinica,2017,4(4):588-598.
APA Kunfeng Wang,Chao Gou,Yanjie Duan,Yilun Lin,Xinhu Zheng,&Fei-Yue Wang.(2017).Generative Adversarial Networks: Introduction and Outlook.IEEE/CAA Journal of Automatica Sinica,4(4),588-598.
MLA Kunfeng Wang,et al."Generative Adversarial Networks: Introduction and Outlook".IEEE/CAA Journal of Automatica Sinica 4.4(2017):588-598.
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