Noise robust chi-square generative adversarial network

被引：0
|
作者：
Li H. [1 ,2 ,3 ,4 ]
Li C. [1 ]
Zhang S. [1 ]
机构：
[1] School of Information Science and Technology, Nantong University, Nantong
[2] State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing
[3] Research Center for Intelligent Information Technology, Nantong University, Nantong
[4] TONGKE School of Microelectronics, Nantong University, Nantong
来源：
| 1600年 / Editorial Board of Journal on Communications卷 / 41期
基金：
中国国家自然科学基金;
关键词：
Chi-square divergence; Generative adversarial network; Image quality; Noise distribution;
D O I：
10.11959/j.issn.1000-436x.2020041
中图分类号：
学科分类号：
摘要：
Aiming at the obvious difference of image quality generated by generative adversarial network under different noises, a chi-square generative adversarial network (CSGAN) was proposed. Combing the advantages of quantification sensitivity and sparse invariance, the chi-square divergence was introduced to calculate the distance between the generated samples and the original samples, which could reduce the influence of different noises on the generated samples and the quality requirement of original samples. Meanwhile, the network architecture was built and the global optimization objective function was constructed to enhance the adversarial performance. Experimental results show that the quality of the images generated by the proposed algorithm has little difference, and the network is more robust to different noises than the state-of-the-art networks. The application of chi-square divergence not only improves the quality of generated images, but also increases the robustness of the network under different noises. © 2020, Editorial Board of Journal on Communications. All right reserved.
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页码：33 / 44
页数：11
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