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

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

林懿伦^1,2,3,,
戴星原^1,2,3,,
李力^4, ,,
王晓^1,3,,
王飞跃^1,5,6,

1.
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北京 100190
2.
中国科学院大学北京 100049
3.
青岛智能产业技术研究院青岛 266109
4.
北京信息科学与技术国家研究中心, 清华大学自动化系北京 100084
5.
国防科学技术大学军事计算实验与平行系统技术中心长沙 410073
6.
中国科学院大学中国经济与社会安全研究中心北京 101408

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

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- 被引次数: 0
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

The New Frontier of AI Research: Generative Adversarial Networks

LIN Yi-Lun^{1,2,3
,},
DAI Xing-Yuan^{1,2,3
,},
LI Li^{4
, ,},
WANG Xiao^{1,3
,},
WANG Fei-Yue^{1,5,6
,}

1.
The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190
2.
University of Chinese Academy of Sciences, Beijing 100049
3.
Qingdao Academy of Intelligent Industries, Qingdao 266109
4.
Department of Automation, Beijing National Research Center for Information Science and Technology(BNRist), Tsinghua University, Beijing 100084
5.
Research Center of Military Computational Experiments and Parallel System, National University of Defense Technology, Changsha 410073
6.
Center of China Economic and Social Security, The University of Chinese Academy of Sciences, Beijing 101408

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

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.
- 深度学习 /
- 生成式对抗网络 /
- 生成模型 /
- 对抗学习 /
- 平行学习
Abstract: Recently, generative adversarial networks (GAN) have become one of the most popular topics in artificial intelligent field. Its outstanding capability of generating realistic samples not only revived the research of generative model, but also inspired the research of semi-supervised learning and unsupervised learning. In this paper, we introduce the basic idea of GAN, and comb its recent development in theory and practice. By concluding its improvements of network structures, optimization methods, the form of the game, the ensemble methods, and its applications, we found the inner logic of its development.
- Deep learning /
- generative adversarial networks /
- generative model /
- adversarial learning /
- parallel learning
注释:

1) 本文责任编委刘德荣

HTML全文

图 1 变分自编码机

Fig. 1 Variational auto-encoder

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图 2 生成式对抗网络

Fig. 2 Generative adversarial networks

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图 3 DCGAN的拓扑结构^[47]

Fig. 3 Schematic of DCGAN architecture^[47]

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图 4 CGAN的拓扑结构

Fig. 4 Schematic of CGAN architecture

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图 5 InfoGAN的拓扑结构

Fig. 5 Schematic of InfoGAN architecture

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图 6 VAE/GAN的拓扑结构

Fig. 6 Schematic of VAE/GAN architecture

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图 7 Stack GAN的拓扑结构

Fig. 7 Schematic of stack GAN architecture

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图 8 LAP-GAN的拓扑结构

Fig. 8 Schematic of LAP-GAN architecture

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图 9 GAN与Actor-critic模型

Fig. 9 GAN and actor-critic models

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图 10 SeqGAN的拓扑结构

Fig. 10 Schematic of SeqGAN architecture

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图 11 WGAN的拓扑结构

Fig. 11 Schematic of WGAN architecture

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图 12 EBGAN的拓扑结

Fig. 12 Schematic of EBGAN architecture

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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]

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图 14 iGAN的生成样例^[110]

Fig. 14 Images generated by iGAN^[110]

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图 15 图对图翻译举例^[112]

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

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图 16 Pix2Pix的拓扑结构

Fig. 16 Chematic of Pix2Pix architectur

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图 17 CycleGAN的拓扑结构

Fig. 17 Schematic of CycleGAN architecture

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图 18 生成式模仿学习

Fig. 18 Generative adversarial imitation learnin

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图 19 探索与利用

Fig. 19 Explore and exploit

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人工智能研究的新前线：生成式对抗网络

doi: 10.16383/j.aas.2018.y000002

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

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The New Frontier of AI Research: Generative Adversarial Networks

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

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人工智能研究的新前线：生成式对抗网络

doi: 10.16383/j.aas.2018.y000002

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

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The New Frontier of AI Research: Generative Adversarial Networks

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

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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