Noise robust chi-square generative adversarial network

Hongjun LI; Chaobo LI; Shibing ZHANG

doi:10.11959/j.issn.1000-436x.2020041

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Noise robust chi-square generative adversarial network

Papers | 更新时间：2024-06-05

- Noise robust chi-square generative adversarial network
- Journal on Communications Vol. 41, Issue 3, Pages: 33-44(2020)
- 作者机构：
  
  1. 南通大学信息科学技术学院，江苏南通 226019
  2. 计算机软件新技术国家重点实验室（南京大学），江苏南京 210093
  3. 南通大学智能信息技术研究中心，江苏南通 226019
  4. 南通大学通科微电子学院，江苏南通 226019
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61871241);Ministry of Education Cooperation in Production and Education(201802302115);Educational Science Research Subject of China Transportation Education Research Association(交教研1802-118);The Science and Technology Program of Nantong(JC2018025);The Science and Technology Program of Nantong(JC2018129);Nantong University-Nantong Joint Research Center for Intelligent Information Technology(KFKT2017B04);Nanjing University State Key Laboratory for Novel Software Technology(KFKT2019B15);Postgraduate Research and Practice Innovation Program of Jiangsu Province(KYCX19_2056)
- DOI：10.11959/j.issn.1000-436x.2020041
  CLC： TP391.41
- Online First：2020-03，
  
  Published：25 March 2020
- 稿件说明：
移动端阅览
Hongjun LI, Chaobo LI, Shibing ZHANG. Noise robust chi-square generative adversarial network[J]. Journal on Communications, 2020, 41(3): 33-44.
DOI：

Hongjun LI, Chaobo LI, Shibing ZHANG. Noise robust chi-square generative adversarial network[J]. Journal on Communications, 2020, 41(3): 33-44. DOI： 10.11959/j.issn.1000-436x.2020041.

摘要

针对不同分布噪声下生成对抗网络生成样本质量差异明显的问题，提出了一种噪声稳健性的卡方生成对抗网络。所提网络结合了卡方散度量化敏感性和稀疏不变性的优势，引入卡方散度计算生成样本分布和真实样本分布的距离，减小不同噪声对生成样本的影响且降低对真实样本的质量要求；搭建了网络架构，构建全局优化目标函数，促进网络不断优化并增强博弈的有效性。实验结果表明，所提网络在不同噪声下的生成样本质量和稳健性优于目前几种主流网络，且图像质量差异较小。卡方散度的引入不仅提高了生成样本质量，而且提升了网络在不同噪声下的稳健性。

Abstract

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.

关键词

Keywords

references

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