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人工智能研究的新前线:生成式对抗网络

林懿伦 戴星原 李力 王晓 王飞跃

林懿伦, 戴星原, 李力, 王晓, 王飞跃. 人工智能研究的新前线:生成式对抗网络. 自动化学报, 2018, 44(5): 775-792. doi: 10.16383/j.aas.2018.y000002
引用本文: 林懿伦, 戴星原, 李力, 王晓, 王飞跃. 人工智能研究的新前线:生成式对抗网络. 自动化学报, 2018, 44(5): 775-792. doi: 10.16383/j.aas.2018.y000002
LIN Yi-Lun, DAI Xing-Yuan, LI Li, WANG Xiao, WANG Fei-Yue. The New Frontier of AI Research: Generative Adversarial Networks. ACTA AUTOMATICA SINICA, 2018, 44(5): 775-792. doi: 10.16383/j.aas.2018.y000002
Citation: LIN Yi-Lun, DAI Xing-Yuan, LI Li, WANG Xiao, WANG Fei-Yue. The New Frontier of AI Research: Generative Adversarial Networks. ACTA AUTOMATICA SINICA, 2018, 44(5): 775-792. doi: 10.16383/j.aas.2018.y000002

人工智能研究的新前线:生成式对抗网络

doi: 10.16383/j.aas.2018.y000002
基金项目: 

国家自然科学基金 61702519

北京市科技项目 D17110600030000

国家自然科学基金 61533019

北京市科技项目 ZC179074Z

详细信息
    作者简介:

    林懿伦  中国科学院自动化研究所复杂系统管理与控制国家重点实验室博士研究生.主要研究方向为社会计算, 智能交通系统和智能汽车, 深度学习和强化学习.E-mail:linyilun2014@ia.ac.cn

    戴星原  中国科学院自动化研究所复杂系统管理与控制国家重点实验室博士研究生.主要研究方向为智能交通系统, 机器学习和深度学习.E-mail:daixingyuan2015@ia.ac.cn

    王晓  中国科学院自动化研究所复杂系统管理与控制国家重点实验室助理研究员.主要研究方向为社会计算, 社会网络结构分析及其内容挖掘, 知识自动化, 人工智能, 平行驾驶.E-mail:x.wang@ia.ac.cn

    王飞跃  中国科学院自动化研究所复杂系统管理与控制国家重点实验室研究员.国防科学技术大学军事计算实验与平行系统技术研究中心主任.主要研究方向为智能系统和复杂系统的建模、分析与控制.E-mail:feiyue.wang@ia.ac.cn

    通讯作者:

    李力  清华大学自动化系副教授.主要研究方向为人工智能和机器学习, 智能交通系统和智能汽车.本文通信作者.E-mail:li-li@tsinghua.edu.cn

The New Frontier of AI Research: Generative Adversarial Networks

Funds: 

National Natural Science Foundation of China 61702519

Beijing Municipal Science and Technology Commission Program D17110600030000

National Natural Science Foundation of China 61533019

Beijing Municipal Science and Technology Commission Program ZC179074Z

More Information
    Author Bio:

     Ph. D. candidate at the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences. His research interest covers social computing, intelligent transportation systems and intelligent vehicles, deep learning and reinforcement learning

     Ph. D. candidate at the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences. His research interest covers intelligent transportation systems, machine learning and deep learning

       Assistant researcher at the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences. Her research interest covers social computing, knowledge automation, artificial intelligence, and parallel driving

     Professor at the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences. Director of the Research Center for Computational Experiments and Parallel Systems Technology, National University of Defense Technology. His research interest covers modeling, analysis, and control of intelligent systems and complex systems

    Corresponding author: LI Li  Associate professor at the Department of Automation, Tsinghua University. His research interest covers artificial intelligence and machine learning, intelligent transportation systems and intelligent vehicles. Corresponding author of this paper
  • 摘要: 生成式对抗网络(Generative adversarial networks,GAN)是当前人工智能学界最为重要的研究热点之一.其突出的生成能力不仅可用于生成各类图像和自然语言数据,还启发和推动了各类半监督学习和无监督学习任务的发展.本文概括了GAN的基本思想,并对近年来相关的理论与应用研究进行了梳理,总结了GAN常见的网络结构与训练方法,博弈形式,集成方法,并对一些应用场景进行了介绍.在此基础上,本文对GAN发展的内在逻辑进行了归纳总结.
    1)  本文责任编委 刘德荣
  • 图  1  变分自编码机

    Fig.  1  Variational auto-encoder

    图  2  生成式对抗网络

    Fig.  2  Generative adversarial networks

    图  3  DCGAN的拓扑结构[47]

    Fig.  3  Schematic of DCGAN architecture[47]

    图  4  CGAN的拓扑结构

    Fig.  4  Schematic of CGAN architecture

    图  5  InfoGAN的拓扑结构

    Fig.  5  Schematic of InfoGAN architecture

    图  6  VAE/GAN的拓扑结构

    Fig.  6  Schematic of VAE/GAN architecture

    图  7  Stack GAN的拓扑结构

    Fig.  7  Schematic of stack GAN architecture

    图  8  LAP-GAN的拓扑结构

    Fig.  8  Schematic of LAP-GAN architecture

    图  9  GAN与Actor-critic模型

    Fig.  9  GAN and actor-critic models

    图  10  SeqGAN的拓扑结构

    Fig.  10  Schematic of SeqGAN architecture

    图  11  WGAN的拓扑结构

    Fig.  11  Schematic of WGAN architecture

    图  12  EBGAN的拓扑结

    Fig.  12  Schematic of EBGAN architecture

    图  13  GAN[86]与传统方法[92]的数据填补效果

    Fig.  13  Image completion by GAN[86] and traditional method[92]

    图  14  iGAN的生成样例[110]

    Fig.  14  Images generated by iGAN[110]

    图  15  图对图翻译举例[112]

    Fig.  15  Examples of image to image translation[112]

    图  16  Pix2Pix的拓扑结构

    Fig.  16  Chematic of Pix2Pix architectur

    图  17  CycleGAN的拓扑结构

    Fig.  17  Schematic of CycleGAN architecture

    图  18  生成式模仿学习

    Fig.  18  Generative adversarial imitation learnin

    图  19  探索与利用

    Fig.  19  Explore and exploit

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出版历程
  • 收稿日期:  2018-03-01
  • 录用日期:  2018-05-01
  • 刊出日期:  2018-05-20

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