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Volume 4 Issue 4
Oct.  2017

IEEE/CAA Journal of Automatica Sinica

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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

Generative Adversarial Networks:Introduction and Outlook

doi: 10.1109/JAS.2017.7510583
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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  • 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.

     

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