生成对抗网络研究综述

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

Abstract

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.

Key words: generative adversarial network, generative model, deep learning, mode collapse, distribution similarity measurement, robustness of artificial neural network

CLC Number:

TP183

Zhenglong WANG, Baowen ZHANG. Survey of generative adversarial network[J]. Chinese Journal of Network and Information Security, 2021, 7(4): 68-85.

Figures/Tables 24

References 106

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改进维度	改进方法	衍生模型
	基于隐变量的改进	cGAN^[20]、BiCoGAN^[21]、IcGAN^[22]、InfoGAN^[23]
输入	基于隐空间的改进	DeLiGAN^[24]、NEMGAN^[25]
	其他输入改进方法	FCGAN^[26]
输出	多分类输出	SGAN^[27]、AC-GAN^[28]
	特征输出	InfoGAN^[23]
	集成方法	AdaGAN^[29]
生成器	样本模式区分	MADGAN^[30]、MGAN^[31]
	其他改进方法	MPMGAN^[32]
	集成方法	PacGAN^[33]、GMAN^[34]、DropoutGAN^[35]
判别器	样本模式区分	D2GAN^[36]、StabilizingGAN^[37]
	其他改进方法	EBGAN^[38]、BEGAN^[39]
	多阶段模型	GRAN^[40]、StackGAN^[41]、ProgressGAN^[42]
多模块组合	辅助模块模型	TripleGAN^[43]、ControlGAN^[44]
	其他改进方法	SGAN^[45]、MemoryGAN^[46]
	神经网络结构替换	DCGAN^[7]、CapsuleGAN^[47]
模型交叉	复合生成模型	VAEGAN^[48]、DEGAN^[49]、AAE^[50]、BiGAN^[51]、ALi^[52]、MatAN^[53]
	其他领域思想	SAGAN^[54]、KDGAN^[55]、IRGAN^[56]、LapGAN^[57]、QuGAN^[58]、BayesianGAN^[59]
	基于f-散度的方法	f-GAN^[60]、LSGAN^[61]、EBGAN^[38]
分布距离度量	基于IPM的方法	WGAN^[11]、WGAN-GP^[62]、WGAN-LP^[63]、BWGAN^[64]、CT-GAN^[65]、RWGAN^[66]、CramerGAN^[67]、SN-GAN^[18]、WGAN-div^[68]、McGAN^[69]、GMMN^[70]、MMDGAN^[71]、FisherGAN^[72]
	其他方法	IGAN^[73]、MIX+GAN^[74]、OT-GAN^[75]、MMGAN^[76]
梯度计算过程		MAGAN^[77]、LSGAN^[61]、SoftmaxGAN^[78]、RGAN^[79]

Survey of generative adversarial network

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