Generative Adversarial Networks:Introduction and Outlook

Kunfeng Wang; Chao Gou; Yanjie Duan; Yilun Lin; Xinhu Zheng; Fei-Yue Wang

doi:10.1109/JAS.2017.7510583

Volume 4 Issue 4

Oct. 2017

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

CiteScore: 23.5, Top 2% (Q1)
Google Scholar h5-index: 77， TOP 5

Turn off MathJax

Article Contents

Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2017 > 4(4): 588-598

Kunfeng Wang, Chao Gou, Yanjie Duan, Yilun Lin, Xinhu Zheng and Fei-Yue Wang, "Generative Adversarial Networks:Introduction and Outlook," IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 588-598, Oct. 2017. doi: 10.1109/JAS.2017.7510583

Citation:

Kunfeng Wang, Chao Gou, Yanjie Duan, Yilun Lin, Xinhu Zheng and Fei-Yue Wang, "Generative Adversarial Networks:Introduction and Outlook," IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 588-598, Oct. 2017. doi: 10.1109/JAS.2017.7510583

Citation:

PDF( 20757 KB)

Generative Adversarial Networks:Introduction and Outlook

doi: 10.1109/JAS.2017.7510583

Kunfeng Wang^{1, 2
,},
Chao Gou^{1, 3
,},
Yanjie Duan^{1, 3
,},
Yilun Lin^{1, 3
,},
Xinhu Zheng^4
,,
Fei-Yue Wang^{1, 5
,
,}

1.
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:

the National Natural Science Foundation of China 61533019

the National Natural Science Foundation of China 71232006

the National Natural Science Foundation of China 91520301

More Information

Author Bio:
Kunfeng Wang (M'17) received the Ph.D. degree in control theory and control engineering from the Graduate University of Chinese Academy of Sciences, Beijing, China, in 2008. He joined the Institute of Automation, Chinese Academy of Sciences and became an associate professor at the State Key Laboratory of Management and Control for Complex Systems. From December 2015 to January 2017, he was a visiting scholar at the School of Interactive Computing, Georgia Institute of Technology. (e-mail: kunfeng.wang@ia.ac.cn)

Chao Gou received the bachelor degree in automation from the University of Electronic Science and Technology of China, Chengdu, China, in 2012. He is currently working toward the Ph.D.degree in control theory and control engineering at the University of Chinese Academy of Sciences, Beijing, China.His research interests include intelligent transportation systems, image processing, and pattern recognition. (e-mail: gouchao2012@ia.ac.cn)

Yanjie Duan received the bachelor degree in automation from Northwestern Polytechnical University, Xi'an, China, in 2012. She is currently working toward the Ph.D. degree in control theory and control engineering at the University of Chinese Academy of Sciences, Beijing, China. Her research interests include intelligent transportation systems, machine learning and its application. (e-mail: duanyanjie2012@ia.ac.cn)

Yilun Lin is currently working toward the Ph.D. degree in control theory and control engineering at the University of Chinese Academy of Sciences, Beijing, China. His research interests include social computing, intelligent transportation systems, deep learning and reinforcement learning. (e-mail: linyilun2014@ia.ac.cn)

Xinhu Zheng received the B.S. degree in control science and engineering from Zhejiang University, Hangzhou, China, in 2011. He is currently working toward the Ph.D. degree in computer science and engineering at the University of Minnesota, Minneapolis, MN, USA. His research interests include social computing, machine learning, and data analytics. (e-mail: zheng473@umn.edu)

Fei-Yue Wang (S'87-M'89-SM'94-F'03) received the Ph.D. degree in computer and systems engineering from Rensselaer Polytechnic Institute, Troy, New York in 1990. He joined the University of Arizona in 1990 and became a professor and Director of the Robotics and Automation Laboratory (RAL) and Program in Advanced Research for Complex Systems (PARCS). In 1999, he founded the Intelligent Control and Systems Engineering Center at the Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China, under the support of the Outstanding Oversea Chinese Talents Program from the State Planning Council and "100 Talent Program" from CAS, and in 2002, was appointed as the Director of the Key Laboratory of Complex Systems and Intelligence Science, CAS. In 2011, he became the State Specially Appointed Expert and the Director of the State Key Laboratory of Management and Control for Complex Systems. Dr. Wang's current research focuses on methods and applications for parallel systems, social computing, and knowledge automation. He was the Founding Editor-in-Chief of the International Journal of Intelligent Control and Systems (1995-2000), Founding EiC of IEEE ITS Magazine (2006-2007), EiC of IEEE Intelligent Systems (2009-2012), and EiC of IEEE Transactions on ITS (2009-2016). Currently he is EiC of China's Journal of Command and Control. Since 1997, he has served as General or Program Chair of more than 20 IEEE, INFORMS, ACM, ASME conferences. He was the President of IEEE ITS Society (2005-2007), Chinese Association for Science and Technology (CAST, USA) in 2005, the American Zhu Kezhen Education Foundation (2007-2008), and the Vice President of the ACM China Council (2010-2011). Since 2008, he is the Vice President and Secretary General of Chinese Association of Automation. Dr. Wang is elected Fellow of IEEE, INCOSE, IFAC, ASME, and AAAS. In 2007, he received the 2nd Class National Prize in Natural Sciences of China and awarded the Outstanding Scientist by ACM for his work in intelligent control and social computing. He received IEEE ITS Outstanding Application and Research Awards in 2009 and 2011, and IEEE SMC Norbert Wiener Award in 2014. (e-mail: feiyue.wang@ia.ac.cn)
Corresponding author: Fei-Yue Wang, e-mail:feiyue.wang@ia.ac.cn
Recommended by Associate Editor Derong Liu
Received Date: 2017-04-09
Accepted Date: 2017-05-17

Abstract

Abstract

Recently, generative adversarial networks (GANs) have become a research focus of artificial intelligence. Inspired by two-player zero-sum game, GANs comprise a generator and a discriminator, both trained under the adversarial learning idea. The goal of GANs is to estimate the potential distribution of real data samples and generate new samples from that distribution. Since their initiation, GANs have been widely studied due to their enormous prospect for applications, including image and vision computing, speech and language processing, etc. In this review paper, we summarize the state of the art of GANs and look into the future. Firstly, we survey GANs' proposal background, theoretic and implementation models, and application fields. Then, we discuss GANs' advantages and disadvantages, and their development trends. In particular, we investigate the relation between GANs and parallel intelligence, with the conclusion that GANs have a great potential in parallel systems research in terms of virtual-real interaction and integration. Clearly, GANs can provide substantial algorithmic support for parallel intelligence.
- ACP approach,
- adversarial learning,
- generative adversarial networks (GANs),
- generative models,
- parallel intelligence,
- zero-sum game

FullText(HTML)

Recommended by Associate Editor Derong Liu

References(64)

References

[1]	I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, "Generative adversarial nets, " in Advances in Neural Information Processing Systems 27, Montreal, Quebec, Canada, 2014, pp. 2672-2680. http://dl.acm.org/citation.cfm?id=2969125
[2]	I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. New York, USA:MIT Press, 2016.
[3]	A. Radford, L. Metz, and S. Chintala, "Unsupervised representation learning with deep convolutional generative adversarial networks, " arXiv: 1511. 06434, 2015. http://www.oalib.com/paper/4054347
[4]	L. J. Ratliff, S. A. Burden, and S. S. Sastry, "Characterization and computation of local Nash equilibria in continuous games, " in Proc. 51st Annu. Allerton Conf. Communication, Control, and Computing (Allerton), Monticello, IL, USA, 2013, pp. 917-924. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6736623
[5]	I. Goodfellow, "NIPS 2016 tutorial: generative adversarial networks, " arXiv: 1701. 00160, 2016. http://arxiv.org/abs/1701.00160
[6]	J. W. Li, W. Monroe, T. L. Shi, S. Jean, A. Ritter, and D. Jurafsky, "Adversarial learning for neural dialogue generation, " arXiv: 1701. 06547, 2017. http://arxiv.org/abs/1701.06547
[7]	L. T. Yu, W. N. Zhang, J. Wang, and Y. Yu, "SeqGAN: sequence generative adversarial nets with policy gradient, " arXiv: 1609. 05473, 2016. http://arxiv.org/abs/1609.05473
[8]	W. W. Hu and Y. Tan, "Generating adversarial malware examples for black-box attacks based on GAN, " arXiv: 1702. 05983, 2017. http://arxiv.org/abs/1702.05983
[9]	M. Chidambaram and Y. J. Qi, "Style transfer generative adversarial networks: Learning to play chess differently, " arXiv: 1702. 06762, 2017. http://arxiv.org/abs/1702.06762
[10]	Y. Bengio, "Learning deep architectures for AI, " Found. Trends Mach. Learn. , vol. 2, no. 1, pp. 1-127, Jan. 2009. http://dl.acm.org/citation.cfm?id=1658423.1658424&coll=DL&dl=GUIDE&CFID=358358199&CFTOKEN=27365575
[11]	D. P. Kingma and M. Welling, "Auto-encoding variational Bayes, " arXiv: 1312. 6114, 2013. http://www.oalib.com/paper/4042889
[12]	D. J. Rezende, S. Mohamed, and D. Wierstra, "Stochastic backpropagation and approximate inference in deep generative models, " arXiv: 1401. 4082, 2014. http://arxiv.org/abs/1401.4082
[13]	G. E. Hinton, T. J. Sejnowski, and D. H. Ackley, "Boltzmann machines: constraint satisfaction networks that learn, " Carnegiemellon Univ. , Pittsburgh, PA, USA, Tech. Rep. CMU-CS-84-119, 1984. https://www.researchgate.net/publication/239574281_Boltzmann_machines_Constraint_satisfaction_networks_that_learn
[14]	D. H. Ackley, G. E. Hinton, and T. J. Sejnowski, "A learning algorithm for Boltzmann machines, " Cognit. Sci. , vol. 9, no. 1, pp. 147-169, Jan. 1985. http://www.sciencedirect.com/science/article/pii/S0364021385800124
[15]	G. E. Hinton, S. Osindero, and Y. W. Teh, "A fast learning algorithm for deep belief nets, " Neural Computat. , vol. 18, no. 7, pp. 1527-1554, May 2006.
[16]	Y. Bengio, É. Thibodeau-Laufer, G. Alain, and J. Yosinski, "Deep generative stochastic networks trainable by backprop, " arXiv: 1306. 1091, 2013. http://arxiv.org/abs/1306.1091
[17]	G. E. Hinton and R. R. Salakhutdinov, "Reducing the dimensionality of data with neural networks, " Science, vol. 313, no. 5786, pp. 504-507, Jul. 2006. http://www.ncbi.nlm.nih.gov/pubmed/16873662
[18]	Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning, " Nature, vol. 521, no. 7553, pp. 436-444, May 2015. http://www.nature.com/nature/journal/v521/n7553/abs/nature14539.html
[19]	A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks, " in Proc. 25th Int. Conf. Neural Information Processing Systems, Lake Tahoe, Nevada, USA, 2012, pp. 1097-1105. http://dl.acm.org/citation.cfm?id=2999257
[20]	K. M. He, X. Y. Zhang, S. Q. Ren, and J. Sun, "Deep residual learning for image recognition, " in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016, pp. 770-778. https://www.computer.org/csdl/proceedings/cvpr/2016/8851/00/8851a770-abs.html
[21]	G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. R. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, "Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups, " IEEE Signal Processing Magazine, vol. 29, no. 6, pp. 82-97, Nov. 2012. http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=6296526
[22]	I. Sutskever, O. Vinyals, and Q. V. Le, "Sequence to sequence learning with neural networks, " in Advances in Neural Information Processing Systems 27: Annu. Conf. Neural Information Processing Systems 2014, Montreal, Quebec, Canada, 2014, pp. 3104-3112. http://dl.acm.org/citation.cfm?id=2969033.2969173
[23]	D. He, W. Chen, L. W. Wang, and T. Y. Liu, "A game-theoretic machine learning approach for revenue maximization in sponsored search, " arXiv: 1406. 0728, 2014. http://dl.acm.org/citation.cfm?id=2540160
[24]	D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, "Mastering the game of Go with deep neural networks and tree search, " Nature, vol. 529, no. 7587, pp. 484-489, Jan. 2016. http://www.ncbi.nlm.nih.gov/pubmed/26819042
[25]	J. Schmidhuber, "Learning factorial codes by predictability minimization, " Neural Computat. , vol. 4, no. 6, pp. 863-879, Nov. 1992. http://dl.acm.org/citation.cfm?id=159784
[26]	Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, "Domain-adversarial training of neural networks, " J. Mach. Learn. Res. , vol. 17, no. 1, pp. 2096-2030, Jan. 2016. http://dl.acm.org/citation.cfm?id=2946704
[27]	W. Z. Chen, H. Wang, Y. Y. Li, H. Su, Z. H. Wang, C. H. Tu, D. Lischinski, D. Cohen-Or, and B. Q. Chen, "Synthesizing training images for boosting human 3D pose estimation, " in Proc. 4th Int. Conf. 3D Vision (3DV), Stanford, CA, USA, 2016, pp. 479-488. http://arxiv.org/abs/1604.02703
[28]	C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, "Intriguing properties of neural networks, " arXiv: 1312. 6199, 2014. http://arxiv.org/abs/1312.6199
[29]	P. McDaniel, N. Papernot, and Z. B. Celik, "Machine learning in adversarial settings, " IEEE Secur. Priv. , vol. 14, no. 3, pp. 68-72, May-Jun. 2016. doi: 10.1109/MSP.2016.51
[30]	X. L. Wang, A. Shrivastava, and A. Gupta, "A-Fast-RCNN: hard positive generation via adversary for object detection, " arXiv: 1704. 03414, 2017. http://arxiv.org/abs/1704.03414
[31]	M. Arjovsky, S. Chintala, and L. Bottou, "Wasserstein GAN, " arXiv: 1701. 07875, 2017.
[32]	G. J. Qi, "Loss-sensitive generative adversarial networks on Lipschitz densities, " arXiv: 1701. 06264, 2017. http://arxiv.org/abs/1701.06264
[33]	A. Odena, "Semi-supervised learning with generative adversarial networks, " arXiv: 1606. 01583, 2016. http://arxiv.org/abs/1606.01583
[34]	M. Mirza and S. Osindero, "Conditional generative adversarial nets, " arXiv: 1411. 1784, 2014. http://www.oalib.com/paper/4066323
[35]	J. Donahue, P. Krähenbühl, and T. Darrell, "Adversarial feature learning, " arXiv: 1605. 09782, 2017. http://arxiv.org/abs/1605.09782
[36]	X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel, "InfoGAN: interpretable representation learning by information maximizing generative adversarial nets, " in Proc. 30th Conf. Neural Information Processing Systems, Barcelona, Spain, 2016, pp. 2172-2180. http://arxiv.org/abs/1606.03657
[37]	A. Odena, C. Olah, and J. Shlens, "Conditional image synthesis with auxiliary classifier GANs, " arXiv: 1610. 09585, 2017. http://arxiv.org/abs/1610.09585
[38]	D. Berthelot, T. Schumm, and L. Metz, "BEGAN: boundary equilibrium generative adversarial networks, " arXiv: 1703. 10717, 2017. http://arxiv.org/abs/1703.10717
[39]	I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville, "Improved training of Wasserstein GANs, " arXiv: 1704. 00028, 2017. http://arxiv.org/abs/1704.00028
[40]	C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. H. Wang, and W. Z. Shi, "Photo-realistic single image super-resolution using a generative adversarial network, " arXiv: 1609. 04802, 2017. http://arxiv.org/abs/1609.04802
[41]	E. Santana and G. Hotz, "Learning a driving simulator, " arXiv: 1608. 01230, 2016. http://arxiv.org/abs/1608.01230
[42]	C. Gou, Y. Wu, K. Wang, F. -Y. Wang, and Q. Ji, "Learning-by-synthesis for accurate eye detection, " in Proc. 23rd Int. Conf. Pattern Recognition (ICPR), Cancun, Mexico, 2016, pp. 3362-3367. http://ieeexplore.ieee.org/document/7900153/
[43]	C. Gou, Y. Wu, K. Wang, K. F. Wang, F. -Y. Wang, and Q. Ji, "A joint cascaded framework for simultaneous eye detection and eye state estimation, " Pattern Recognit. , vol. 67, pp. 23-31, Jul. 2017. http://www.sciencedirect.com/science/article/pii/S0031320317300250
[44]	A. Shrivastava, T. Pfister, O. Tuzel, J. Susskind, W. D. Wang, and R. Webb, "Learning from simulated and unsupervised images through adversarial training, " arXiv: 1612. 07828, 2016. http://arxiv.org/abs/1612.07828
[45]	R. Huang, S. Zhang, T. Y. Li, and R. He, "Beyond face rotation: global and local perception GAN for photorealistic and identity preserving frontal view synthesis, " arXiv: 1704. 04086, 2017. http://arxiv.org/abs/1704.04086
[46]	J. Y. Zhu, T. Park, P. Isola, and A. A. Efros, "Unpaired image-to-image translation using cycle-consistent adversarial networks, " arXiv: 1703. 10593, 2017. http://arxiv.org/abs/1703.10593
[47]	Y. Z. Zhang, Z. Gan, and L. Carin, "Generating text via adversarial training, " Proc. Workshop on Adversarial Training, Barcelona, Spain, 2016.
[48]	S. Pascual, A. Bonafonte, and J. Serrá, "SEGAN: speech enhancement generative adversarial network, " arXiv: 1703. 09452, 2017. http://arxiv.org/abs/1703.09452
[49]	S. Reed, Z. Akata, X. C. Yan, L. Logeswaran, B. Schiele, and H. Lee, "Generative adversarial text to image synthesis, " in Proc. 33rd Int. Conf. Machine Learning, New York, NY, USA, vol. 48, pp. 1060-1069, 2016. http://dl.acm.org/citation.cfm?id=3045503
[50]	H. Zhang, T. Xu, H. S. Li, S. T. Zhang, X. L. Huang, X. G. Wang, and D. Metaxas, "StackGAN: text to photo-realistic image synthesis with stacked generative adversarial networks, " arXiv: 1612. 03242, 2016. http://arxiv.org/abs/1612.03242
[51]	J. Ho and S. Ermon, "Generative adversarial imitation learning, " in Proc. 30th Conf. Neural Information Processing Systems, Barcelona, Spain, 2016, pp. 4565-4573. http://arxiv.org/abs/1606.03476
[52]	C. Finn, P. Christiano, P. Abbeel, and S. Levine, "A connection between generative adversarial networks, inverse reinforcement learning, and energy-based models, " arXiv: 1611. 03852, 2016. http://arxiv.org/abs/1611.03852
[53]	D. Pfau and O. Vinyals, "Connecting generative adversarial networks and actor-critic methods, " arXiv: 1610. 01945, 2017. http://arxiv.org/abs/1610.01945
[54]	E. Choi, S. Biswal, B. Malin, J. Duke, W. F. Stewart, and J. M. Sun, "Generating multi-label discrete electronic health records using generative adversarial networks, " arXiv: 1703. 06490, 2017. http://arxiv.org/abs/1703.06490v1
[55]	F. -Y. Wang, "Parallel system methods for management and control of complex systems, " Control Dec. , vol. 19, no. 5, pp. 485-489, 514, May 2004. http://en.cnki.com.cn/Article_en/CJFDTotal-KZYC200405001.htm
[56]	F. -Y. Wang, "Computational experiments for behavior analysis and decision evaluation of complex systems, " J. Syst. Simulat. vol. 16, no. 5, pp. 893-897, May 2004. http://en.cnki.com.cn/Article_en/CJFDTotal-XTFZ200405008.htm
[57]	F. -Y. Wang, J. Zhang, Q. L. Wei, X. H. Zheng, and L. Li, "PDP: parallel dynamic programming, " IEEE/CAA J. Automat. Sinica, vol. 4, no. 1, pp. 1-5, Jan. 2017. http://ieeexplore.ieee.org/document/7815546/
[58]	T. X. Bai, S. Wang, Z. Shen, D. P. Cao, N. N. Zheng, and F. -Y. Wang, "Parallel robotics and parallel unmanned systems: framework, structure, process, platform and applications, " Acta Automat. Sinica, vol. 43, no. 2, pp. 161-175, Feb. 2017. http://www.en.cnki.com.cn/Article_en/CJFDTOTAL-MOTO201702001.htm
[59]	F. -Y. Wang, X. Wang, L. X. Li, and L. Li, "Steps toward parallel intelligence, " IEEE/CAA J. Automat. Sinica, vol. 3, no. 4, pp. 345-348, Oct. 2016. http://ieeexplore.ieee.org/document/7589480/
[60]	K. F. Wang, C. Gou, and F. -Y. Wang, "Parallel vision: an ACP-based approach to intelligent vision computing, " Acta Automat. Sinica, vol. 42, no. 10, pp. 1490-1500, Oct. 2016. http://www.aas.net.cn/EN/Y2016/V42/I10/1490
[61]	F. -Y. Wang, "On the modeling, analysis, control and management of complex systems, " Complex Syst. Complex. Sci. , vol. 3, no. 2, pp. 26-34, Jun. 2006. http://en.cnki.com.cn/Article_en/CJFDTotal-FZXT200602003.htm
[62]	F. -Y. Wang, D. R. Liu, G. Xiong, C. J. Cheng, and D. B. Zhao, "Parallel control theory of complex systems and applications, " Complex Syst. Complex. Sci. , vol. 9, no. 3, pp. 1-12, Sep. 2012. http://en.cnki.com.cn/Article_en/CJFDTOTAL-FZXT201203002.htm
[63]	F. -Y. Wang, "Parallel control: a method for data-driven and computational control, " Acta Automat. Sinica, vol. 39, no. 4, pp. 293-302, Apr. 2013. http://en.cnki.com.cn/Article_en/CJFDTOTAL-MOTO201304002.htm
[64]	L. Li, Y. L. Lin, D. P. Cao, N. N. Zheng, and F. -Y. Wang, "Parallel learning—a new framework for machine learning, " Acta Automat. Sinica, vol. 43, no. 1, pp. 1-8, Jan. 2017. http://www.en.cnki.com.cn/Article_en/CJFDTOTAL-MOTO201701002.htm

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(10)

Get Citation

PDF

XML

Article Metrics

Article views (2647) PDF downloads(378)

Generative Adversarial Networks:Introduction and Outlook

doi: 10.1109/JAS.2017.7510583

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Export File

Citation

Format

Content