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生成式对抗网络:从生成数据到创造智能

王坤峰 左旺孟 谭营 秦涛 李力 王飞跃

王坤峰, 左旺孟, 谭营, 秦涛, 李力, 王飞跃. 生成式对抗网络:从生成数据到创造智能. 自动化学报, 2018, 44(5): 769-774. doi: 10.16383/j.aas.2018.y000001
引用本文: 王坤峰, 左旺孟, 谭营, 秦涛, 李力, 王飞跃. 生成式对抗网络:从生成数据到创造智能. 自动化学报, 2018, 44(5): 769-774. doi: 10.16383/j.aas.2018.y000001
Wang Kun-Feng, Zuo Wang-Meng, Tan Ying, Qin Tao, Li Li, Wang Fei-Yue. Generative adversarial networks: from generating data to creating intelligence. ACTA AUTOMATICA SINICA, 2018, 44(5): 769-774. doi: 10.16383/j.aas.2018.y000001
Citation: Wang Kun-Feng, Zuo Wang-Meng, Tan Ying, Qin Tao, Li Li, Wang Fei-Yue. Generative adversarial networks: from generating data to creating intelligence. ACTA AUTOMATICA SINICA, 2018, 44(5): 769-774. doi: 10.16383/j.aas.2018.y000001

生成式对抗网络:从生成数据到创造智能

doi: 10.16383/j.aas.2018.y000001
基金项目: 

国家自然科学基金 91720000

国家自然科学基金 61533019

详细信息
    作者简介:

    左旺孟  哈尔滨工业大学计算机科学与技术学院教授.主要研究方向为计算机视觉, 机器学习, 生物特征识别.E-mail:wmzuo@hit.edu.cn

    谭营  北京大学信息科学技术学院教授.主要研究方向为计算智能, 群体智能, 机器学习.E-mail:ytan@pku.edu.cn

    秦涛  微软亚洲研究院资深研究员, 主要研究方向为机器学习, 博弈论和多智能体系统, 搜索和在线广告.E-mail:taoqin@microsoft.com

    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.E-mail:li-li@tsinghua.edu.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.国防科技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:

    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.本文通信作者.E-mail:kunfeng.wang@ia.ac.cn

Generative adversarial networks: from generating data to creating intelligence

Funds: 

National Natural Science Foundation of China 91720000

National Natural Science Foundation of China 61533019

More Information
    Author Bio:

     Professor at the School of Computer Science and Technology, Harbin Institute of Technology. His research interest covers computer vision, machine learning, and biometrics

     Professor at the School of Electronics Engineering and Computer Science, Peking University. His research interest covers computational intelligence, swarm intelligence, and machine learning

     Senior researcher at Microsoft Research Asia. His research interest covers machine learning, game theory and multi-agent system, search and advertising

     Associate professor at Department of Automation, Tsinghua University. His research interest covers artificial intelligence and machine learning, intelligent transportation systems and intelligent vehicles

     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: WANG Kun-Feng  Associate professor 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, intelligent visual computing, and machine learning. Corresponding author of this paper
  • 图  1  GAN的基本结构和计算流程

    Fig.  1  Basic structure and computation procedure of GAN

    图  2  arXiv上GAN论文数量的变化趋势

    Fig.  2  Trend of the number of GAN papers published on arXiv

    图  3  arXiv上GAN论文所属的TOP 10学科领域

    Fig.  3  Top 10 subject categories of the GAN papers published on arXiv

  • [1] Goodfellow I J, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial nets. In: Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal, Canada: Curran Associates, Inc., 2014. 2672-2680
    [2] Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath A A. Generative adversarial networks:an overview. IEEE Signal Processing Magazine, 2018, 35(1):53-65 doi: 10.1109/MSP.2017.2765202
    [3] 王坤峰, 苟超, 段艳杰, 林懿伦, 郑心湖, 王飞跃.生成式对抗网络GAN的研究进展与展望.自动化学报, 2017, 43(3):321-332 http://www.aas.net.cn/CN/abstract/abstract19012.shtml

    Wang Kun-Feng, Gou Chao, Duan Yan-Jie, Lin Yi-Lun, Zheng Xin-Hu, Wang Fei-Yue. Generative adversarial networks:the state of the art and beyond. Acta Automatica Sinica, 2017, 43(3):321-332 http://www.aas.net.cn/CN/abstract/abstract19012.shtml
    [4] Arjovsky M, Chintala S, Bottou L. Wasserstein GAN. arXiv preprint arXiv: 1701. 07875, 2017.
    [5] Zhu J Y, Park T, Isola P, Efros A A. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy: IEEE, 2017. 2242-2251
    [6] Karras T, Aila T, Laine S, Lehtinen J. Progressive growing of GANs for improved quality, stability, and variation. arXiv preprint arXiv: 1710. 10196, 2017.
    [7] 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 30th Conference on Neural Information Processing Systems. Barcelona, Spain: Curran Associates, Inc., 2016.
    [8] Zhang H, Xu T, Li H S, Zhang S T, Huang X L, Wang X G, et al. StackGAN: text to photo-realistic image synthesis with stacked generative adversarial networks. arXiv preprint arXiv: 1612. 03242, 2016.
    [9] Zhu J Y, Krähenbühl P, Shechtman E, Efros A A. Generative visual manipulation on the natural image manifold. arXiv preprint arXiv: 1609. 03552, 2016.
    [10] Ledig C, Theis L, Huszar F, Caballero J, Cunningham A, Acosta A, et al. Photo-realistic single image super-resolution using a generative adversarial network. arXiv preprint arXiv: 1609. 04802, 2016.
    [11] Santana E, Hotz G. Learning a driving simulator. arXiv preprint arXiv: 1608. 01230, 2016.
    [12] Shrivastava A, Pflster T, Tuzel O, Susskind J, Wang W D, Webb R. Learning from simulated and unsupervised images through adversarial training. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA: IEEE, 2017. 2242-2251
    [13] Liu M Y, Breuel T, Kautz J. Unsupervised image-to-image translation networks. In: Advances in Neural Information Processing Systems 30. Barcelona, Spain: Curran Associates, Inc., 2017.
    [14] Wu J J, Zhang C K, Xue T F, Freeman B, Tenenbaum J. Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. In: Advances in Neural Information Processing Systems 29. Barcelona, Spain: Curran Associates, Inc., 2016.
    [15] Luc P, Couprie C, Chintala S, Verbeek J. Semantic segmentation using adversarial networks. arXiv preprint arXiv: 1611. 08408, 2016.
    [16] Hu W W, Tan Y. Generating adversarial malware examples for black-box attacks based on GAN. arXiv preprint arXiv: 1702. 05983, 2017.
    [17] 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
    [18] 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://mall.cnki.net/magazine/Article/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://mall.cnki.net/magazine/Article/KZYC200405001.htm
    [19] 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
    [20] Wang K F, Gou C, Zheng N N, Rehg J M, Wang F Y. Parallel vision for perception and understanding of complex scenes:methods, framework, and perspectives. Artificial Intelligence Review, 2017, 48(3):299-329 doi: 10.1007/s10462-017-9569-z
    [21] 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
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出版历程
  • 收稿日期:  2018-05-01
  • 刊出日期:  2018-05-20

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