Generative Adversarial Networks:Introduction and Outlook

Kunfeng Wang; Chao Gou; Yanjie Duan; Yilun Lin; Xinhu Zheng; Fei-Yue Wang

doi:10.1109/JAS.2017.7510583

Volume 4 Issue 4

Oct. 2017

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2017 > 4(4): 588-598

Kunfeng Wang, Chao Gou, Yanjie Duan, Yilun Lin, Xinhu Zheng and Fei-Yue Wang, "Generative Adversarial Networks:Introduction and Outlook," IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 588-598, Oct. 2017. doi: 10.1109/JAS.2017.7510583

Citation:

Kunfeng Wang, Chao Gou, Yanjie Duan, Yilun Lin, Xinhu Zheng and Fei-Yue Wang, "Generative Adversarial Networks:Introduction and Outlook," IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 588-598, Oct. 2017. doi: 10.1109/JAS.2017.7510583

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Generative Adversarial Networks:Introduction and Outlook

doi: 10.1109/JAS.2017.7510583

Kunfeng Wang^{1, 2
,},
Chao Gou^{1, 3
,},
Yanjie Duan^{1, 3
,},
Yilun Lin^{1, 3
,},
Xinhu Zheng^4
,,
Fei-Yue Wang^{1, 5
,
,}

1.
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.
Qingdao Academy of Intelligent Industries, Qingdao 266000, China
3.
University of Chinese Academy of Sciences, Beijing 100049, China
4.
Department of Computer Science and Engineering, University of Minnesota, Minneapolis MN 55414, USA
5.
Research Center for Computational Experiments and Parallel Systems Technology, National University of Defense Technology, Changsha 410073, China

Funds:

the National Natural Science Foundation of China 61533019

the National Natural Science Foundation of China 71232006

the National Natural Science Foundation of China 91520301

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Author Bio:
Kunfeng Wang (M'17) received the Ph.D. degree in control theory and control engineering from the Graduate University of Chinese Academy of Sciences, Beijing, China, in 2008. He joined the Institute of Automation, Chinese Academy of Sciences and became an associate professor at the State Key Laboratory of Management and Control for Complex Systems. From December 2015 to January 2017, he was a visiting scholar at the School of Interactive Computing, Georgia Institute of Technology. (e-mail: kunfeng.wang@ia.ac.cn)

Chao Gou received the bachelor degree in automation from the University of Electronic Science and Technology of China, Chengdu, China, in 2012. He is currently working toward the Ph.D.degree in control theory and control engineering at the University of Chinese Academy of Sciences, Beijing, China.His research interests include intelligent transportation systems, image processing, and pattern recognition. (e-mail: gouchao2012@ia.ac.cn)

Yanjie Duan received the bachelor degree in automation from Northwestern Polytechnical University, Xi'an, China, in 2012. She is currently working toward the Ph.D. degree in control theory and control engineering at the University of Chinese Academy of Sciences, Beijing, China. Her research interests include intelligent transportation systems, machine learning and its application. (e-mail: duanyanjie2012@ia.ac.cn)

Yilun Lin is currently working toward the Ph.D. degree in control theory and control engineering at the University of Chinese Academy of Sciences, Beijing, China. His research interests include social computing, intelligent transportation systems, deep learning and reinforcement learning. (e-mail: linyilun2014@ia.ac.cn)

Xinhu Zheng received the B.S. degree in control science and engineering from Zhejiang University, Hangzhou, China, in 2011. He is currently working toward the Ph.D. degree in computer science and engineering at the University of Minnesota, Minneapolis, MN, USA. His research interests include social computing, machine learning, and data analytics. (e-mail: zheng473@umn.edu)

Fei-Yue Wang (S'87-M'89-SM'94-F'03) received the Ph.D. degree in computer and systems engineering from Rensselaer Polytechnic Institute, Troy, New York in 1990. He joined the University of Arizona in 1990 and became a professor and Director of the Robotics and Automation Laboratory (RAL) and Program in Advanced Research for Complex Systems (PARCS). In 1999, he founded the Intelligent Control and Systems Engineering Center at the Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China, under the support of the Outstanding Oversea Chinese Talents Program from the State Planning Council and "100 Talent Program" from CAS, and in 2002, was appointed as the Director of the Key Laboratory of Complex Systems and Intelligence Science, CAS. In 2011, he became the State Specially Appointed Expert and the Director of the State Key Laboratory of Management and Control for Complex Systems. Dr. Wang's current research focuses on methods and applications for parallel systems, social computing, and knowledge automation. He was the Founding Editor-in-Chief of the International Journal of Intelligent Control and Systems (1995-2000), Founding EiC of IEEE ITS Magazine (2006-2007), EiC of IEEE Intelligent Systems (2009-2012), and EiC of IEEE Transactions on ITS (2009-2016). Currently he is EiC of China's Journal of Command and Control. Since 1997, he has served as General or Program Chair of more than 20 IEEE, INFORMS, ACM, ASME conferences. He was the President of IEEE ITS Society (2005-2007), Chinese Association for Science and Technology (CAST, USA) in 2005, the American Zhu Kezhen Education Foundation (2007-2008), and the Vice President of the ACM China Council (2010-2011). Since 2008, he is the Vice President and Secretary General of Chinese Association of Automation. Dr. Wang is elected Fellow of IEEE, INCOSE, IFAC, ASME, and AAAS. In 2007, he received the 2nd Class National Prize in Natural Sciences of China and awarded the Outstanding Scientist by ACM for his work in intelligent control and social computing. He received IEEE ITS Outstanding Application and Research Awards in 2009 and 2011, and IEEE SMC Norbert Wiener Award in 2014. (e-mail: feiyue.wang@ia.ac.cn)
Corresponding author: Fei-Yue Wang, e-mail:feiyue.wang@ia.ac.cn
Recommended by Associate Editor Derong Liu
Received Date: 2017-04-09
Accepted Date: 2017-05-17

Abstract

Abstract

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.
- ACP approach,
- adversarial learning,
- generative adversarial networks (GANs),
- generative models,
- parallel intelligence,
- zero-sum game

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Recommended by Associate Editor Derong Liu

References(64)

References

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Generative Adversarial Networks:Introduction and Outlook

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