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生成对抗网络在各领域应用研究进展

刘建伟 谢浩杰 罗雄麟

刘建伟, 谢浩杰, 罗雄麟. 生成对抗网络在各领域应用研究进展. 自动化学报, 2020, 46(12): 2500−2536 doi: 10.16383/j.aas.c180831
引用本文: 刘建伟, 谢浩杰, 罗雄麟. 生成对抗网络在各领域应用研究进展. 自动化学报, 2020, 46(12): 2500−2536 doi: 10.16383/j.aas.c180831
Liu Jian-Wei, Xie Hao-Jie, Luo Xiong-Lin. Research progress on application of generative adversarial networks in various fields. Acta Automatica Sinica, 2020, 46(12): 2500−2536 doi: 10.16383/j.aas.c180831
Citation: Liu Jian-Wei, Xie Hao-Jie, Luo Xiong-Lin. Research progress on application of generative adversarial networks in various fields. Acta Automatica Sinica, 2020, 46(12): 2500−2536 doi: 10.16383/j.aas.c180831

生成对抗网络在各领域应用研究进展

doi: 10.16383/j.aas.c180831
基金项目: 国家自然科学基金(21676295), 中国石油大学(北京) 2018年度前瞻导向及培育项目“神经网络深度学习理论框架和分析方法及工具” (2462018QZDX02)资助
详细信息
    作者简介:

    刘建伟:博士, 中国石油大学(北京)副研究员. 主要研究方向为智能信息处理, 机器学习, 复杂系统分析, 预测与控制, 算法分析与设计. 本文通信作者. E-mail: liujw@cup.edu.cn

    谢浩杰:中国石油大学(北京)信息科学与工程学院硕士研究生. 主要研究方向为机器学习. E-mail: xhj19941116@163.com

    罗雄麟:博士, 中国石油大学(北京)教授. 主要研究方向为智能控制和复杂系统分析, 预测与控制. E-mail: luoxl@cup.edu.cn

Research Progress on Application of Generative Adversarial Networks in Various Fields

Funds: Supported by National Natural Science Foundation of China (21676295) and Science Foundation of China University of Petroleum Beijing (2462018QZDX02)
  • 摘要: 随着深度学习的快速发展, 生成式模型领域也取得了显著进展. 生成对抗网络(Generative adversarial network, GAN)是一种无监督的学习方法, 它是根据博弈论中的二人零和博弈理论提出的. GAN具有一个生成器网络和一个判别器网络, 并通过对抗学习进行训练. 近年来, GAN成为一个炙手可热的研究方向. GAN不仅在图像领域取得了不错的成绩, 还在自然语言处理(Natural language processing, NLP)以及其他领域崭露头角. 本文对GAN的基本原理、训练过程和传统GAN存在的问题进行了阐述, 进一步详细介绍了通过损失函数的修改、网络结构的变化以及两者结合的手段提出的GAN变种模型的原理结构, 其中包括: 条件生成对抗网络(Conditional GAN, CGAN)、基于Wasserstein 距离的生成对抗网络(Wasserstein-GAN, WGAN)及其基于梯度策略的WGAN (WGAN-gradient penalty, WGAN-GP)、基于互信息理论的生成对抗网络(Informational-GAN, InfoGAN)、序列生成对抗网络(Sequence GAN, SeqGAN)、Pix2Pix、循环一致生成对抗网络(Cycle-consistent GAN, Cycle GAN)及其增强Cycle-GAN (Augmented CycleGAN). 概述了在计算机视觉、语音与NLP领域中基于GAN和相应GAN变种模型的基本原理结构, 其中包括: 基于CGAN的脸部老化应用(Face aging CGAN, Age-cGAN)、双路径生成对抗网络(Two-pathway GAN, TP-GAN)、表示解析学习生成对抗网络(Disentangled representation learning GAN, DR-GAN)、对偶学习生成对抗网络(DualGAN)、GeneGAN、语音增强生成对抗网络(Speech enhancement GAN, SEGAN)等. 介绍了GAN在医学、数据增强等领域的应用情况, 其中包括: 数据增强生成对抗网络(Data augmentation GAN, DAGAN)、医学生成对抗网络(Medical GAN, MedGAN)、无监督像素级域自适应方法(Unsupervised pixel-level domain adaptation method, PixelDA). 最后对GAN未来发展趋势及方向进行了展望.
  • 图  1  VAE + GAN结构

    Fig.  1  The structure of VAE + GAN

    图  2  GAN训练过程

    Fig.  2  Training process of GAN

    图  3  CGAN结构

    Fig.  3  The structure of CGAN

    图  4  DCGAN生成器网络结构

    Fig.  4  The structure of DCGAN's generator

    图  5  互信息图

    Fig.  5  Mutual information map

    图  6  InfoGAN结构

    Fig.  6  The structure of InfoGAN

    图  7  SeqGAN结构

    Fig.  7  The structure of SeqGAN

    图  8  Pix2Pix结构

    Fig.  8  The structure of Pix2Pix

    图  9  CycleGAN原理图

    Fig.  9  Principle of CycleGAN

    图  10  Augmented CycleGAN原理图

    Fig.  10  Principle of augmented CycleGAN

    图  11  Age-cGAN原理图

    Fig.  11  Principle of Age-cGAN

    图  12  TP-GAN生成器结构原理图

    Fig.  12  Principle of TP-GAN's generator

    图  13  TP-GAN实验效果图

    Fig.  13  Experiment results of TP-GAN

    图  14  DR-GAN结构图(单图像)

    Fig.  14  The structure of DR-GAN (single image)

    图  15  DR-GAN生成器结构图(多图像)

    Fig.  15  The structure of DR-GAN's generator (mutiple image)

    图  16  SGAN结构原理图

    Fig.  16  The structure of SGAN

    图  17  SRGAN实验效果

    Fig.  17  Experiment result of SRGAN

    图  18  DualGAN结构

    Fig.  18  The structure of DualGAN

    图  19  GeneGAN训练过程

    Fig.  19  Training process of GeneGAN

    图  20  S2-GAN结构

    Fig.  20  The structure of S2-GAN

    图  21  Text to image GAN结构

    Fig.  21  The structure of text to image GAN

    图  22  GAN应用于图像语义分割

    Fig.  22  GAN applied to image semantic segmentation

    图  23  自动画家模型效果

    Fig.  23  Experiment result of auto-painter

    图  24  Dual motion GAN结构

    Fig.  24  The structure of dual motion GAN

    图  25  S-GAN结构

    Fig.  25  The structure of S-GAN

    图  26  双语字典GAN结构

    Fig.  26  The structure of bilingual lexicon GAN

    图  27  ADAN结构

    Fig.  27  The structure of ADAN

    表  1  GAN模型变种

    Table  1  Variant of GAN model

    年份模型
    2014条件生成对抗网络 (CGAN)[10]
    2015深卷积生成对抗网络 (DCGAN)[7]
    2017Wasserstein-GAN (WGAN)[12]
    2017具有梯度惩罚项 (WGAN-GP)[14]
    2016信息生成对抗网络 (InfoGAN)[15]
    2017序列生成对抗网络 (SeqGAN)[16]
    2017基于CGAN的图像到图像翻译模型 (Pix2Pix)[18]
    2017循环生成对抗网络 (CycleGAN)[20]
    2018增强循环生成对抗网络 (Augmented CycleGAN)[22]
    下载: 导出CSV

    表  2  GAN在图像领域的应用

    Table  2  GAN's application in the field of computer vision

    内容模型
    人脸图像识别与图像生成基于 CGAN 的人脸识别模型[28], Age-cGAN[29], GLCA-GAN[30], TP-GAN[31], DR-GAN[33], SGAN[34],
    MGAN[35], BigGAN[37]
    图像超分辨率SRGAN[38], c-CycleGAN[39]
    图像复原与多视角图像生成基于 GAN 的语义图像修复模型[41], PGGAN[42], VariGAN[45]
    图像转换DualGAN[47], GeneGAN[48], S2-GAN[49], DA-GAN[50]
    文本描述到图像生成Text to image GAN[52], GAWWN[53], RTT-GAN[54]
    图像语义分割基于GAN的语义分割模型[55-56], Contrast-GAN[57]
    图像着色Auto-painter[58], DCGAN 用于图像着色[59]
    视频预测 基于GAN的下帧图像生成模型[61], 利用 3D-CNN 作为生成器的 GAN[62], Dual motion GAN[63]
    视觉显著性预测SalGAN[64], MC-GAN[65]
    图像密写S-GAN[66]
    3D 图像生成3D-GAN[67], VON[68]
    下载: 导出CSV

    表  3  GAN在语音与NLP领域的应用

    Table  3  GAN's application in the field of speech and NLP

    内容模型
    语音增强SEGAN[69], 基于 Pix2Pix 的语音增强模型[71]
    音乐生成MuseGAN[72]
    语音识别基于 GAN 的语音识别模型[73], 基于多任务对抗学习模式的语音识别模型[74], WGAN 用于语音识别[75], VoiceGAN[76], MTGAN[77], Residual GAN[78]
    对话模型的评估与生成基于 SeqGAN 的对话评估模型[79], 基于 SeqGAN 的对话生成模型[80]
    生成离散序列Gumbel-softmax GAN[82]
    双语字典基于 GAN 的双语字典模型[83]
    文本分类与生成对抗多任务学习模型[86], 基于 WGAN 的文本生成模型[87], DP-GAN[88]
    语篇分析ADAN[89]
    机器翻译BR-CSGAN[92], Multi-CSGAN-NMT[93], Adversarial-NMT[94], BGAN-NMT[95]
    下载: 导出CSV

    表  4  GAN在其他领域的应用

    Table  4  GAN's application in other fields

    内容模型
    人体姿态估计基于 RL 与 GAN 的姿态估计模型[96], 基于 GAN 的姿态估计模型[97], 基于双向 LSTM 的 CGAN 模型[98]
    恶意软件检测MalGAN[99]
    数据集标记与数据增强基于 GAN 的仿真无监督学习框架[100], RenderGAN[101], DAGAN[102]
    物理应用基于 GAN 的高能粒子物理图像生成模型[104]
    医学领域RefineGAN[105], 基于 CGAN 的多对比度 MRI 图像生成模型[106], MedGAN[107], 基于 GAN 的视网膜血管图像生成模型[108], 基于 WGAN 的 CCTA 模型[109]
    隐私保护基于 GAN 的用户信息攻击模型[110]
    域适应学习领域PixelDA[112], 基于 GAN 的域自适应分类任务[113], 基于 GAN 的域间联合嵌入特征空间模型[114]
    自动驾驶基于 GAN 的驾驶场景预测模型[115], 基于 VAE 与 GAN 的路况预测模型[116]
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-12-13
  • 录用日期:  2019-06-06
  • 网络出版日期:  2020-12-29
  • 刊出日期:  2020-12-29

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