This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Yilun Lin, Xingyuan Dai, li Li, et al. The new frontier of artificial intelligence research:generative adversarial networks[J]. IEEE/CAA Journal of Automatica Sinica (JAS), 2018, 44(05):775-792.YilunLinXingyuanDailiLiThe new frontier of artificial intelligence research:generative adversarial networks[J]IEEE/CAA Journal of Automatica Sinica (JAS)201844(05):775792Search in Google Scholar
Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. The Journal of Machine Learning Research, 2014, 15(1): 1929-1958.SrivastavaNHintonGKrizhevskyADropout: a simple way to prevent neural networks from overfitting[J]The Journal of Machine Learning Research201415(1):19291958Search in Google Scholar
Gatys L A, Ecker A S, Bethge M. Image style transfer using convolutional neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 2414-2423.GatysL AEckerA SBethgeM.Image style transfer using convolutional neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition20162414242310.1109/CVPR.2016.265Search in Google Scholar
Kunfeng Wang, Chao Gou, Yanjie Duan, et al. Research progress and prospect of Generative adversarial networks[J] IEEE/CAA Journal of Automatica Sinica (JAS), 2017, 43(03):321-332.KunfengWangChaoGouYanjieDuanResearch progress and prospect of Generative adversarial networks[J]IEEE/CAA Journal of Automatica Sinica (JAS)201743(03):321332Search in Google Scholar
Bengio Y, Laufer E, Alain G, et al. Deep generative stochastic networks trainable by backprop[C]//International Conference on Machine Learning. 2014: 226-234.BengioYLauferEAlainGDeep generative stochastic networks trainable by backprop[C]//International Conference on Machine Learning2014226234Search in Google Scholar
Opper M, Archambeau C. The variational Gaussian approximation revisited[J]. Neural computation, 2009, 21(3): 786-792.OpperMArchambeauC.The variational Gaussian approximation revisited[J]Neural computation200921(3):78679210.1162/neco.2008.08-07-59218785854Search in Google Scholar
Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]//Advances in neural information processing systems. 2014: 2672-2680.GoodfellowIPouget-AbadieJMirzaMGenerative adversarial nets[C]//Advances in neural information processing systems201426722680Search in Google Scholar
Ratliff L J, Burden S A, Sastry S S. Characterization and computation of local nash equilibria in continuous games[C]//2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2013: 917-924.RatliffL JBurdenS ASastryS S.Characterization and computation of local nash equilibria in continuous games[C]//2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton)IEEE201391792410.1109/Allerton.2013.6736623Search in Google Scholar
Xianlun Tang, Yiming Du, Yuwei Liu et al. An image recognition method based on conditional depth convolution Generative adversarial networks[J] IEEE/CAA Journal of Automatica Sinica (JAS), 2018, 44(05):855-864.TangXianlunDuYimingLiuYuweiAn image recognition method based on conditional depth convolution Generative adversarial networks[J]IEEE/CAA Journal of Automatica Sinica (JAS)201844(05):855864Search in Google Scholar
Zhu J Y, Park T, Isola P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 2223-2232.ZhuJ YParkTIsolaPUnpaired image-to-image translation using cycle-consistent adversarial networks[C]//Proceedings of the IEEE International Conference on Computer Vision20172223223210.1109/ICCV.2017.244Search in Google Scholar
Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks[J]. arXiv preprint arXiv:1511.06434, 2015.RadfordAMetzLChintalaS.Unsupervised representation learning with deep convolutional generative adversarial networks[J]arXiv preprint arXiv:1511.064342015Search in Google Scholar
Kingma D P, Ba J. Adam: A method for stochastic optimization[J]. arXiv preprint arXiv:1412.6980, 2014.KingmaD PAdamBa J.A method for stochastic optimization[J]arXiv preprint arXiv:1412.69802014Search in Google Scholar
Salimans T, Goodfellow I, Zaremba W, et al. Improved techniques for training gans[C]//Advances in neural information processing systems. 2016: 2234-2242.SalimansTGoodfellowIZarembaWImproved techniques for training gans[C]//Advances in neural information processing systems201622342242Search in Google Scholar
Gulrajani I, Ahmed F, Arjovsky M, et al. Improved training of wasserstein gans[C]//Advances in Neural Information Processing Systems. 2017: 5767-5777GulrajaniIAhmedFArjovskyMImproved training of wasserstein gans[C]//Advances in Neural Information Processing Systems201757675777Search in Google Scholar