Research progress on generative adversarial network

Zhang Enqi1,2
Gu Guanghua1,2
Zhao Chen1,2
Zhao Zhiming1,2
1. School of Information Science & Engineering, Yanshan University, Qinhuangdao Hebei 066004, China
2. Key Laboratory of Information Transmission & Signal Processing of Hebei Province, Qinhuangdao Hebei 066004, China

Abstract

The significance of the GAN model based on the zero-sum game idea is that the data distribution can be obtained through unsupervised learning, and it can generate more realistic data. It can be applied in many fields, especially the field of computer vision, and achieved great results in image generation, so that it becomes a hot spot in current research. This paper took the GAN model and its application results in specific fields as the research object, conducted extensive research on the improvement and expansion of GAN research results, and discussed from multiple practical application areas such as image super-resolution reconstruction and text synthesis pictures. It systematically sorted out and summarized the advantages and disadvantages of GAN, predicted and analyzed the development trend and application prospect of GAN in combination with natural language processing and reinforcement learning.

Foundation Support

河北省自然科学基金资助项目(F2017203169)
河北省高等学校科学研究重点项目(ZD2017080)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.05.0095
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 4
Section: Survey
Pages: 968-974
Serial Number: 1001-3695(2021)04-002-0968-07

Publish History

[2021-04-05] Printed Article

Cite This Article

张恩琪, 顾广华, 赵晨, 等. 生成对抗网络GAN的研究进展 [J]. 计算机应用研究, 2021, 38 (4): 968-974. (Zhang Enqi, Gu Guanghua, Zhao Chen, et al. Research progress on generative adversarial network [J]. Application Research of Computers, 2021, 38 (4): 968-974. )

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