生成对抗网络研究综述

王正龙; 张保稳

doi:10.11959/j.issn.2096-109x.2021080

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生成对抗网络研究综述

专栏Ⅰ：网络攻防技术 | 更新时间：2024-06-03

- 生成对抗网络研究综述
- Survey of generative adversarial network
- 网络与信息安全学报 2021年7卷第4期页码：68-85
- 作者机构：
  
  1. 上海交通大学网络安全技术研究院，上海 200240
  2. 上海市信息安全综合管理技术研究重点实验室，上海 200240
- 作者简介：
  
  [ "王正龙（1997− ），男，宁夏银川人，上海交通大学硕士生，主要研究方向为生成对抗网络、人工神经网络鲁棒性与安全性" ]
  [ "张保稳（1975− ），男，山东菏泽人，上海交通大学副研究员，主要研究方向为神经网络鲁棒性与安全性、网络信息安全本体，以及新型网络系统安全性分析与评估" ]
- 基金信息：
  
  国家重点研发计划;The National Key R＆D Program of China(2020YFB1807504;2020YFB1807500)
- DOI：10.11959/j.issn.2096-109x.2021080
  中图分类号： TP183
- 网络首发：2021-08，
  
  纸质出版：2021-08-15
- 稿件说明：
移动端阅览
王正龙, 张保稳. 生成对抗网络研究综述[J]. 网络与信息安全学报, 2021,7(4):68-85.

Zhenglong WANG, Baowen ZHANG. Survey of generative adversarial network[J]. Chinese Journal of Network and Information Security, 2021, 7(4): 68-85.
王正龙, 张保稳. 生成对抗网络研究综述[J]. 网络与信息安全学报, 2021,7(4):68-85. DOI： 10.11959/j.issn.2096-109x.2021080.

Zhenglong WANG, Baowen ZHANG. Survey of generative adversarial network[J]. Chinese Journal of Network and Information Security, 2021, 7(4): 68-85. DOI： 10.11959/j.issn.2096-109x.2021080.

摘要

首先介绍了生成对抗网络基本理论、应用场景和研究现状，并列举了其亟待改进的问题。围绕针对提升模型训练效率、提升生成样本质量和降低模式崩溃现象发生可能性3类问题的解决，从模型结构和训练过程两大改进方向和7个细分维度，对近年来生成对抗网络的主要研究工作、改进机理和特点进行了归纳和总结，并结合3方面对其未来的研究方向进行了探讨。

Abstract

Firstly

the basic theory

application scenarios and current state of research of GAN (generative adversarial network) were introduced

and the problems need to be improved were listed.Then

recent research

improvement mechanism and model features in 2 categories and 7 subcategories revolved around 3 points (improving model training efficiency

improving the quality of generated samples

and reducing the possibility of model collapse) were generalized and summarized.Finally

3 future research directions were discussed.

关键词

Keywords

references

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