生成式对抗网络GAN的研究进展与展望

王坤峰; 苟超; 段艳杰; 林懿伦; 郑心湖; 王飞跃

doi:10.16383/j.aas.2017.y000003

生成式对抗网络GAN的研究进展与展望

doi: 10.16383/j.aas.2017.y000003

王坤峰^1,2,,
苟超^1,3,,
段艳杰^1,3,,
林懿伦^1,3,,
郑心湖^4,,
王飞跃^1,5, ,

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 中国
2.
青岛智能产业技术研究院青岛 266000 中国
3.
中国科学院大学北京 100049 中国
4.
明尼苏达大学计算机科学与工程学院明尼阿波利斯 MN 55414 美国
5.
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长沙 410073 中国

基金项目:

国家自然科学基金 61533019

国家自然科学基金 91520301

国家自然科学基金 71232006

详细信息

作者简介:
王坤峰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副研究员.主要研究方向为智能交通系统, 智能视觉计算, 机器学习.E-mail:kunfeng.wang@ia.ac.cn

苟超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, 图像处理, 模式识别.E-mail:gouchao2012@ia.ac.cn

段艳杰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, 机器学习及应用.E-mail:duanyanjie2012@ia.ac.cn

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, 深度学习和强化学习.E-mail:linyilun2014@ia.ac.cn

郑心湖明尼苏达大学计算机科学与工程学院研究生.主要研究方向为社会计算, 机器学习, 数据分析.E-mail:zheng473@umn.edu

通讯作者:
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.本文通信作者. E-mail:feiyue.wang@ia.ac.cn

计量
- 文章访问数: 11647
- HTML全文浏览量: 3592
- PDF下载量: 7940
- 被引次数: 0
出版历程
- 收稿日期: 2017-02-01
- 录用日期: 2017-03-01
- 刊出日期: 2017-03-20

Generative Adversarial Networks: The State of the Art and Beyond

WANG Kun-Feng^{1,2
,},
GOU Chao^{1,3
,},
DUAN Yan-Jie^{1,3
,},
LIN Yi-Lun^{1,3
,},
ZHENG Xin-Hu^4
,,
WANG Fei-Yue^{1,5
, ,}

1.
The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.
Qingdao Academy of Intelligent Industries, Qingdao 266000, China
3.
University of Chinese Academy of Sciences, Beijing 100049, China
4.
Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55414, USA
5.
Research Center for Computational Experiments and Parallel Systems Technology, National University of Defense Technology, Changsha 410073, China

Funds:

National Natural Science Foundation of China 61533019

National Natural Science Foundation of China 91520301

National Natural Science Foundation of China 71232006

More Information

Author Bio:
Associate professor at The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. His research interest covers intelligent transportation systems, intelligent vision computing, and machine learning

Ph. D. candidate at The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. His research interest covers intelligent transportation systems, image processing, and pattern recognition

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

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

Postgraduate in the Department of Computer Science and Engineering, University of Minnesota, USA. His research interest covers social computing, machine learning, and data analytics

Corresponding author: WANG Fei-YueProfessor at The State Key Laboratory of Management and Control for 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 of this paper

摘要

摘要: 生成式对抗网络GAN（Generative adversarial networks）目前已经成为人工智能学界一个热门的研究方向.GAN的基本思想源自博弈论的二人零和博弈，由一个生成器和一个判别器构成，通过对抗学习的方式来训练.目的是估测数据样本的潜在分布并生成新的数据样本.在图像和视觉计算、语音和语言处理、信息安全、棋类比赛等领域，GAN正在被广泛研究，具有巨大的应用前景.本文概括了GAN的研究进展，并进行展望.在总结了GAN的背景、理论与实现模型、应用领域、优缺点及发展趋势之后，本文还讨论了GAN与平行智能的关系，认为GAN可以深化平行系统的虚实互动、交互一体的理念，特别是计算实验的思想，为ACP（Artificial societies，computational experiments，and parallel execution）理论提供了十分具体和丰富的算法支持.
- 生成式对抗网络 /
- 生成式模型 /
- 零和博弈 /
- 对抗学习 /
- 平行智能 /
- ACP方法
Abstract: Generative adversarial networks (GANs) have become a hot research topic in artificial intelligence. Inspired by the two-player zero-sum game, GAN is composed of a generator and a discriminator, both trained with the adversarial learning mechanism. The aim of GAN is to estimate the potential distribution of existing data and generate new data samples from the same distribution. Since its initiation, GAN has been widely studied due to its enormous prospect for applications, including image and vision computing, speech and language processing, information security, and chess game. In this paper we summarize the state of the art of GAN and look into its future. First of all, we survey the GAN's background, theoretic and implementation models, application fields, advantages and disadvantages, and development trends. Then, we investigate the relation between GAN and parallel intelligence with the conclusion that GAN has a great potential in parallel systems especially in computational experiments, in terms of virtual-real interaction and integration. Finally, we clarify that GAN can provide specific and substantial algorithmic support for the ACP theory.
- Generative adversarial networks /
- generative models /
- zero-sum game /
- adversarial learning /
- parallel intelligence /
- ACP methodology
注释:

1) 本文责任编委刘德荣

HTML全文

图 1 人工智能的研究层次

Fig. 1 The levels of artificial intelligence

下载: 全尺寸图片幻灯片

图 2 GAN的计算流程与结构

Fig. 2 Computation procedure and structure of GAN

下载: 全尺寸图片幻灯片

图 3 GAN衍生模型的计算流程与结构 ((a) GAN^[1], W-GAN^[29], LS-GAN^[30]; (b) Semi-GAN^[31]; (c) C-GAN^[32]; (d) Bi-GAN^[33]; (e) Info-GAN^[34]; (f) AC-GAN^[35]; (g) Seq-GAN^[6])

Fig. 3 Computation procedures and structures of GAN-derived models

下载: 全尺寸图片幻灯片

图 4 基于GAN的生成图像示例^[36]

Fig. 4 Illustration of GAN-generated image^[36]

下载: 全尺寸图片幻灯片

图 5 基于GAN的生成图像示例 (奇数列为生成图像, 偶数列为目标图像)^[38]

Fig. 5 Another illustration of GAN-generated images (Odd columns show the generated images, and even columns show the target images)^[38]

下载: 全尺寸图片幻灯片

图 6 平行视觉的基本框架与体系结构^[52]

Fig. 6 Basic framework and architecture for parallel vision^[52]

下载: 全尺寸图片幻灯片

图 7 平行控制系统的结构^[52]

Fig. 7 Structure of parallel control systems^[52]

下载: 全尺寸图片幻灯片

图 8 平行学习的理论框架图^[56]

Fig. 8 Theoretical framework of parallel learning^[52]

下载: 全尺寸图片幻灯片

参考文献(56)

[1]	Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. In: Proceedings of the 2014 Conference on Advances in Neural Information Processing Systems 27. Montreal, Canada: Curran Associates, Inc., 2014. 2672-2680
[2]	Goodfellow I, Bengio Y, Courville A. Deep Learning. Cambridge, UK: MIT Press, 2016.
[3]	Ratliff L J, Burden S A, Sastry S S. Characterization and computation of local Nash equilibria in continuous games. In: Proceedings of the 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton). Monticello, IL, USA: IEEE, 2013. 917-924
[4]	Goodfellow I. NIPS 2016 tutorial: generative adversarial networks. arXiv preprint arXiv: 1701.00160, 2016.
[5]	Li J W, Monroe W, Shi T L, Jean S, Ritter A, Jurafsky D. Adversarial learning for neural dialogue generation. arXiv preprint arXiv: 1701.06547, 2017.
[6]	Yu L T, Zhang W N, Wang J, Yu Y. SeqGAN: sequence generative adversarial nets with policy gradient. arXiv preprint arXiv: 1609.05473, 2016.
[7]	Hu WW, Tan Y. Generating adversarial malware examples for black-box attacks based on GAN. arXiv preprint arXiv: 1702.05983, 2017.
[8]	Chidambaram M, Qi Y J. Style transfer generative adversarial networks: learning to play chess differently. arXiv preprint arXiv: 1702.06762, 2017.
[9]	Bengio Y. Learning deep architectures for AI. Foundations and Trends in Machine Learning, 2009, 2(1): 1-127 doi: 10.1561/2200000006
[10]	Kingma D P, Welling M. Auto-encoding variational Bayes. arXiv preprint arXiv: 1312.6114, 2013.
[11]	Rezende D J, Mohamed S, Wierstra D. Stochastic backpropagation and approximate inference in deep generative models. arXiv preprint arXiv: 1401.4082, 2014.
[12]	Hinton G E, Sejnowski T J, Ackley D H. Boltzmann Machines: Constraint Satisfaction Networks that Learn. Technical Report No. CMU-CS-84-119, Carnegie-Mellon University, Pittsburgh, PA, USA, 1984.
[13]	Ackley D H, Hinton G E, Sejnowski T J. A learning algorithm for Boltzmann machines. Cognitive Science, 1985, 9(1): 147-169 doi: 10.1207/s15516709cog0901_7
[14]	Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets. Neural Computation, 2006, 18(7): 1527-1554 doi: 10.1162/neco.2006.18.7.1527
[15]	Bengio Y, Thibodeau-Laufer É, Alain G, Yosinski J. Deep generative stochastic networks trainable by backprop. arXiv preprint arXiv: 1306.1091, 2013.
[16]	Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504-507 http://web.inf.ufpr.br/menotti/ci171-2015-2-1/files/seminario-Victor-slides.pdf
[17]	LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444 doi: 10.1038/nature14539
[18]	Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada, USA: ACM, 2012. 1097-1105
[19]	He K M, Zhang X Y, Ren S Q, Sun J. Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA: IEEE, 2016. 770-778
[20]	Hinton G, Deng L, Yu D, Dahl G E, Mohamed A R, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath T N, Kingsbury B. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Processing Magazine, 2012, 29(6): 82-97 doi: 10.1109/MSP.2012.2205597
[21]	Sutskever I, Vinyals O, Le Q V. Sequence to sequence learning with neural networks. In: Proceedings of the 2014 Conference on Advances in Neural Information Processing Systems 27. Montreal, Canada: Curran Associates, Inc., 2014. 3104-3112.
[22]	He D, Chen W, Wang L W, Liu T Y. A game-theoretic machine learning approach for revenue maximization in sponsored search. arXiv preprint arXiv: 1406.0728, 2014.
[23]	Silver D, Huang A, Maddison C J, Guez A, Sifre L, van Den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M, Dieleman S, Grewe D, Nham J, Kalchbrenner N, Sutskever I, Lillicrap T, Leach M, Kavukcuoglu K, Graepel T, Hassabis D. Mastering the game of go with deep neural networks and tree search. Nature, 2016, 529(7587): 484-489 doi: 10.1038/nature16961
[24]	Schmidhuber J. Learning factorial codes by predictability minimization. Neural Computation, 1992, 4(6): 863-879 doi: 10.1162/neco.1992.4.6.863
[25]	Ganin Y, Ustinova E, Ajakan H, Germain P, Larochelle H, Laviolette F, Marchand M, Lempitsky V. Domain-adversarial training of neural networks. Journal of Machine Learning Research, 2016, 17(59): 1-35 https://www.researchgate.net/publication/277333816_Domain-Adversarial_Training_of_Neural_Networks
[26]	Chen W Z, Wang H, Li Y Y, Su H, Wang Z H, Tu C H, Lischinski D, Cohen-Or D, Chen B. Synthesizing training images for boosting human 3D pose estimation. In: Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV). Stanford, CA, USA: IEEE, 2016. 479-488
[27]	Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, Fergus R. Intriguing properties of neural networks. arXiv preprint arXiv: 1312.6199, 2013.
[28]	McDaniel P, Papernot N, Celik Z B. Machine learning in adversarial settings. IEEE Security & Privacy, 2016, 14(3): 68-72 https://www.sec.in.tum.de/assets/lehre/ss12/ml/semniartalk.pdf
[29]	Arjovsky M, Chintala S, Bottou L. Wasserstein GAN. arXiv preprint arXiv: 1701.07875, 2017.
[30]	Qi G J. Loss-sensitive generative adversarial networks on Lipschitz densities. arXiv preprint arXiv: 1701.06264, 2017.
[31]	Odena A. Semi-supervised learning with generative adversarial networks. arXiv preprint arXiv: 1606.01583, 2016.
[32]	Mirza M, Osindero S. Conditional generative adversarial nets. arXiv preprint arXiv: 1411.1784, 2014.
[33]	Donahue J, Krähenbühl P, Darrell T. Adversarial feature learning. arXiv preprint arXiv: 1605.09782, 2016.
[34]	Chen X, Duan Y, Houthooft R, Schulman J, Sutskever I, Abbeel P. InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. In: Proceedings of the 2016 Neural Information Processing Systems. Barcelona, Spain: Department of Information Technology IMEC, 2016. 2172-2180
[35]	Odena A, Olah C, Shlens J. Conditional image synthesis with auxiliary classifier GANs. arXiv preprint arXiv: 1610.09585, 2016.
[36]	Ledig C, Theis L, Huszár F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z H, Shi W Z. Photo-realistic single image super-resolution using a generative adversarial network. arXiv preprint arXiv: 1609.04802, 2016.
[37]	Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv: 1409.1556, 2014.
[38]	Santana E, Hotz G. Learning a driving simulator. arXiv preprint arXiv: 1608.01230, 2016.
[39]	Gou C, Wu Y, Wang K, Wang F Y, Ji Q. Learning-by-synthesis for accurate eye detection. In: Proceedings of the 2016 IEEE International Conference on Pattern Recognition (ICPR). Cancun, Mexico: IEEE, 2016.
[40]	Gou C, Wu Y, Wang K, Wang K F, Wang F Y, Ji Q. A joint cascaded framework for simultaneous eye detection and eye state estimation. Pattern Recognition, 2017, 67: 23-31 doi: 10.1016/j.patcog.2017.01.023
[41]	Shrivastava A, Pfister T, Tuzel O, Susskind J, Wang W D, Webb R. Learning from simulated and unsupervised images through adversarial training. arXiv preprint arXiv: 1612.07828, 2016.
[42]	Zhang Y Z, Gan Z, Carin L. Generating text via adversarial training. In: Proceedings of the 2016 Conference on Advances in Neural Information Processing Systems 29. Curran Associates, Inc., 2016.
[43]	Reed S, Akata Z, Yan X C, Logeswaran L, Lee H, Schiele B. Generative adversarial text to image synthesis. In: Proceedings of the 33rd International Conference on Machine Learning. New York, NY, USA: ICML, 2016.
[44]	Ho J, Ermon S. Generative adversarial imitation learning. In: Proceedings of the 2016 Conference on Advances in Neural Information Processing Systems 29. Curran Associates, Inc., 2016. 4565-4573
[45]	Finn C, Christiano P, Abbeel P, Levine S. A connection between generative adversarial networks, inverse reinforcement learning, and energy-based models. arXiv preprint arXiv: 1611.03852, 2016.
[46]	Pfau D, Vinyals O. Connecting generative adversarial networks and actor-critic methods. arXiv preprint arXiv: 1610.01945, 2016.
[47]	王飞跃.平行系统方法与复杂系统的管理和控制.控制与决策, 2004, 19(5): 485-489, 514 http://www.cnki.com.cn/Article/CJFDTOTAL-KZYC200405001.htm Wang Fei-Yue. Parallel system methods for management and control of complex systems. Control and decision, 2004, 19(5): 485-489, 514 http://www.cnki.com.cn/Article/CJFDTOTAL-KZYC200405001.htm
[48]	王飞跃.计算实验方法与复杂系统行为分析和决策评估.系统仿真学报, 2004, 16(5): 893-897 http://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ200405008.htm Wang Fei-Yue. Computational experiments for behavior analysis and decision evaluation of complex systems. Journal of System Simulation, 2004, 16(5): 893-897 http://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ200405008.htm
[49]	Wang F Y, Zhang J, Wei Q L, Zheng X H, Li L. PDP: parallel dynamic programming. IEEE/CAA Journal of Automatica Sinica, 2017, 4(1): 1-5 doi: 10.1109/JAS.2017.7510310
[50]	白天翔, 王帅, 沈震, 曹东璞, 郑南宁, 王飞跃.平行机器人与平行无人系统:框架、结构、过程、平台及其应用.自动化学报, 2017, 43(2): 161-175 http://www.aas.net.cn/CN/abstract/abstract18998.shtml Bai Tian-Xiang, Wang Shuai, Shen Zhen, Cao Dong-Pu, Zheng Nan-Ning, Wang Fei-Yue. Parallel robotics and parallel unmanned systems: framework, structure, process, platform and applications. Acta Automatica Sinica, 2017, 43(2): 161-175 http://www.aas.net.cn/CN/abstract/abstract18998.shtml
[51]	Wang F Y, Wang X, Li L X, Li L. Steps toward parallel intelligence. IEEE/CAA Journal of Automatica Sinica, 2016, 3(4): 345-348 doi: 10.1109/JAS.2016.7510067
[52]	王坤峰, 苟超, 王飞跃.平行视觉:基于ACP的智能视觉计算方法.自动化学报, 2016, 42(10): 1490-1500 http://www.aas.net.cn/CN/abstract/abstract18936.shtml Wang Kun-Feng, Gou Chao, Wang Fei-Yue. Parallel vision: an ACP-based approach to intelligent vision computing. Acta Automatica Sinica, 2016, 42(10): 1490-1500 http://www.aas.net.cn/CN/abstract/abstract18936.shtml
[53]	王飞跃.关于复杂系统的建模、分析、控制和管理.复杂系统与复杂性科学, 2006, 3(2): 26-34 http://www.cnki.com.cn/Article/CJFDTOTAL-FZXT200602003.htm Wang Fei-Yue. On the modeling, analysis, control and management of complex systems. Complex Systems and Complexity Science, 2006, 3(2): 26-34 http://www.cnki.com.cn/Article/CJFDTOTAL-FZXT200602003.htm
[54]	王飞跃, 刘德荣, 熊刚, 程长建, 赵冬斌.复杂系统的平行控制理论及应用.复杂系统与复杂性科学, 2012, 9(3): 1-12 http://www.cnki.com.cn/Article/CJFDTOTAL-FZXT201203002.htm Wang Fei-Yue, Liu De-Rong, Xiong Gang, Cheng Chang-Jian, Zhao Dong-Bin. Parallel control theory of complex systems and applications. Complex Systems and Complexity Science, 2012, 9(3): 1-12 http://www.cnki.com.cn/Article/CJFDTOTAL-FZXT201203002.htm
[55]	王飞跃.平行控制:数据驱动的计算控制方法.自动化学报, 2013, 39(4): 293-302 http://www.aas.net.cn/CN/abstract/abstract17915.shtml Wang Fei-Yue. Parallel control: a method for data-driven and computational control. Acta Automatica Sinica, 2013, 39(4): 293-302 http://www.aas.net.cn/CN/abstract/abstract17915.shtml
[56]	李力, 林懿伦, 曹东璞, 郑南宁, 王飞跃.平行学习-机器学习的一个新型理论框架.自动化学报, 2017, 43(1): 1-8 http://www.aas.net.cn/CN/abstract/abstract18984.shtml Li Li, Lin Yi-Lun, Cao Dong-Pu, Zheng Nan-Ning, Wang Fei-Yue. Parallel learning——a new framework for machine learning. Acta Automatica Sinica, 2017, 43(1): 1-8 http://www.aas.net.cn/CN/abstract/abstract18984.shtml