{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T16:04:49Z","timestamp":1784217889057,"version":"3.55.0"},"reference-count":334,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172090"],"award-info":[{"award-number":["62172090"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172089"],"award-info":[{"award-number":["62172089"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&D Project of China","award":["2021QY2102"],"award-info":[{"award-number":["2021QY2102"]}]},{"name":"CAAI-Huawei MindSpore Open Fund"},{"name":"Alibaba Innovative Research Program"},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2242022R10071"],"award-info":[{"award-number":["2242022R10071"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001381","name":"National Research Foundation Singapore","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001381","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jiangsu Provincial Double-Innovation Doctor Program","award":["JSSCBS20210075"],"award-info":[{"award-number":["JSSCBS20210075"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2023,4,1]]},"DOI":"10.1109\/tkde.2021.3130191","type":"journal-article","created":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T21:38:12Z","timestamp":1637703492000},"page":"3313-3332","source":"Crossref","is-referenced-by-count":912,"title":["A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9450-1759","authenticated-orcid":false,"given":"Jie","family":"Gui","sequence":"first","affiliation":[{"name":"School of Cyber Science and Engineering, Southeast University, Nanjing, Jiangsu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4029-9935","authenticated-orcid":false,"given":"Zhenan","family":"Sun","sequence":"additional","affiliation":[{"name":"Center for Research on Intelligent Perception and Computing, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2751-5114","authenticated-orcid":false,"given":"Yonggang","family":"Wen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7225-5449","authenticated-orcid":false,"given":"Dacheng","family":"Tao","sequence":"additional","affiliation":[{"name":"JD Explore Academy, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jieping","family":"Ye","sequence":"additional","affiliation":[{"name":"Beike, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/Allerton.2013.6736623"},{"key":"ref2","article-title":"Making the world differentiable: On using fully recurrent self-supervised neural networks for dynamic reinforcement learning and planning in non-stationary environments","author":"Schmidhuber","year":"1990","journal-title":"Inst. Comput. Sci., Tech. Univ. Munich, Germany, FKI-126, Tech. Rep."},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/3115.003.0030"},{"key":"ref4","first-page":"98","article-title":"Art & science as by-products of the search for novel patterns, or data compressible in unknown yet learnable ways","author":"Schmidhuber","year":"2009","journal-title":"Edizioni"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1992.4.6.863"},{"key":"ref6","first-page":"2672","article-title":"Generative adversarial nets","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Goodfellow"},{"key":"ref7","article-title":"Unsupervised minimax: Adversarial curiosity, generative adversarial networks, and predictability minimization","author":"Schmidhuber","year":"2020"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.23919\/TST.2017.8195348"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.04.069"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.5120\/ijca2019918334"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2017.7510583"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3301282"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2765202"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3440207"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-20912-4_24"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2905015"},{"key":"ref17","first-page":"2172","article-title":"InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Chen"},{"key":"ref18","article-title":"Conditional generative adversarial nets","author":"Mirza","year":"2014"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01258-8_18"},{"key":"ref20","first-page":"271","article-title":"f-GAN: Training generative neural samplers using variational divergence minimization","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Nowozin"},{"key":"ref21","first-page":"214","article-title":"Wasserstein generative adversarial networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Arjovsky"},{"key":"ref22","first-page":"5767","article-title":"Improved training of wasserstein GANs","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Gulrajani"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01265-2"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.304"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2872043"},{"key":"ref26","first-page":"1","article-title":"Spectral normalization for generative adversarial networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Miyato"},{"key":"ref27","article-title":"Geometric GAN","author":"Lim","year":"2017"},{"key":"ref28","first-page":"2794","article-title":"Hierarchical implicit models and likelihood-free variational inference","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Tran"},{"key":"ref29","first-page":"1","article-title":"Mode regularized generative adversarial networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Che"},{"key":"ref30","first-page":"1","article-title":"Unrolled generative adversarial networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Metz"},{"key":"ref31","first-page":"1","article-title":"The relativistic discriminator: A key element missing from standard GAN","volume-title":"Proc. Int. Conf. Learn. Representation","author":"Jolicoeur-Martineau"},{"key":"ref32","first-page":"2234","article-title":"Improved techniques for training GANs","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Salimans"},{"key":"ref33","first-page":"2642","article-title":"Conditional image synthesis with auxiliary classifier GANs","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Odena"},{"key":"ref34","first-page":"1486","article-title":"Deep generative image models using a laplacian pyramid of adversarial networks","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Denton"},{"key":"ref35","first-page":"1","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Radford"},{"key":"ref36","first-page":"1","article-title":"Progressive growing of GANs for improved quality, stability, and variation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Karras"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.629"},{"key":"ref38","first-page":"7354","article-title":"Self-attention generative adversarial networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref39","first-page":"1","article-title":"Large scale GAN training for high fidelity natural image synthesis","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Brock"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref41","first-page":"1","article-title":"Energy-based generative adversarial network","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhao"},{"key":"ref42","article-title":"BeGAN: Boundary equilibrium generative adversarial networks","author":"Berthelot","year":"2017"},{"key":"ref43","first-page":"1","article-title":"Adversarial feature learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Donahue"},{"key":"ref44","first-page":"1","article-title":"Adversarially learned inference","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Dumoulin"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11449"},{"key":"ref46","first-page":"2670","article-title":"Dual discriminator generative adversarial nets","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Nguyen"},{"key":"ref47","first-page":"1","article-title":"Generative multi-adversarial networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Durugkar"},{"key":"ref48","first-page":"1","article-title":"MGAN: Training generative adversarial nets with multiple generators","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Hoang"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00888"},{"key":"ref50","first-page":"469","article-title":"Coupled generative adversarial networks","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Liu"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2018.00113"},{"key":"ref54","article-title":"SRDGAN: Learning the noise prior for super resolution with dual generative adversarial networks","author":"Guan","year":"2019"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-1398-5_15"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2868350"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.267"},{"key":"ref58","first-page":"406","article-title":"Pose guided person image generation","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Ma"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00524"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01100"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46454-1_36"},{"key":"ref62","first-page":"1","article-title":"Neural photo editing with introspective adversarial networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Brock"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00244"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46487-9_43"},{"key":"ref65","first-page":"1","article-title":"Texture synthesis with spatial generative adversarial networks","volume-title":"Proc. Neural Inf. Process. Syst. Adv. Learn. Workshop","author":"Jetchev"},{"key":"ref66","first-page":"469","article-title":"Learning texture manifolds with the periodic spatial Gan","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Bergmann"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00643"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.211"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_13"},{"key":"ref70","first-page":"613","article-title":"Generating videos with scene dynamics","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Vondrick"},{"key":"ref71","first-page":"4414","article-title":"Unsupervised learning of disentangled representations from video","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Denton"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.361"},{"key":"ref73","first-page":"1152","article-title":"Video-to-video synthesis","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00165"},{"key":"ref75","first-page":"3155","article-title":"Adversarial ranking for language generation","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Lin"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080786"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331218"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00160"},{"key":"ref79","first-page":"1","article-title":"C-RNN-GAN: Continuous recurrent neural networks with adversarial training","volume-title":"Proc. Neural Inf. Process. Syst. Constructive Mach. Learn. Workshop","author":"Mogren"},{"key":"ref80","article-title":"Objective-reinforced generative adversarial networks (ORGAN) for sequence generation models","author":"Guimaraes","year":"2018"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10804"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-7566-5"},{"key":"ref83","first-page":"1","article-title":"Auto-encoding variational Bayes","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kingma"},{"key":"ref84","first-page":"1278","article-title":"Stochastic backpropagation and approximate inference in deep generative models","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Rezende"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-7687-1_31"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1016\/S0364-0213(85)80012-4"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"ref88","first-page":"3387","article-title":"Synthesizing the preferred inputs for neurons in neural networks via deep generator networks","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Nguyen"},{"key":"ref89","first-page":"226","article-title":"Deep generative stochastic networks trainable by backprop","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Bengio"},{"key":"ref90","first-page":"899","article-title":"Generalized denoising auto-encoders as generative models","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Bengio"},{"key":"ref91","article-title":"NIPS 2016 tutorial: Generative adversarial networks","author":"Goodfellow","year":"2017"},{"key":"ref92","first-page":"1","article-title":"PixelCNN++: Improving the pixelCNN with discretized logistic mixture likelihood and other modifications","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Salimans"},{"key":"ref93","first-page":"661","article-title":"Does the wake-sleep algorithm produce good density estimators?","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Frey"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/3348.001.0001"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1038\/nature16961"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00175"},{"key":"ref97","first-page":"1","article-title":"Adversarial examples in the physical world","volume-title":"Proc. Int. Conf. Learn. Representations Workshop","author":"Kurakin"},{"key":"ref98","first-page":"3910","article-title":"Adversarial examples that fool both computer vision and time-limited humans","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Elsayed"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00624"},{"key":"ref100","first-page":"274","article-title":"Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Athalye"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220078"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00957"},{"key":"ref103","first-page":"1","article-title":"Intriguing properties of neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Szegedy"},{"key":"ref104","first-page":"1","article-title":"Explaining and harnessing adversarial examples","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Goodfellow"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1109\/SPW.2018.00014"},{"key":"ref106","first-page":"1","article-title":"Defense-GAN: Protecting classifiers against adversarial attacks using generative models","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Samangouei"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2807385"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8683044"},{"key":"ref109","article-title":"Generative adversarial trainer: Defense to adversarial perturbations with GAN","author":"Lee","year":"2017"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1145\/2046684.2046692"},{"key":"ref111","first-page":"1","article-title":"On distinguishability criteria for estimating generative models","volume-title":"Proc. Int. Conf. Learn. Representations Workshop","author":"Goodfellow"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2018.2864500"},{"key":"ref113","first-page":"1","article-title":"GAN dissection: Visualizing and understanding generative adversarial networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Bau"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3076766"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00164"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3025301"},{"key":"ref117","first-page":"1","article-title":"CausalGAN: Learning causal implicit generative models with adversarial training","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kocaoglu"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2961882"},{"key":"ref119","first-page":"8559","article-title":"Adversarial multiple source domain adaptation","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Zhao"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2905606"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01240-3_31"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2020.2991375"},{"key":"ref123","first-page":"5248","article-title":"A convex duality framework for GANs","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Farnia"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1145\/3240508.3240509"},{"key":"ref125","first-page":"1","article-title":"On the \u201dsteerability\u201d of generative adversarial networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Jahanian"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2020.2983698"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114431"},{"key":"ref128","article-title":"LoGAN: Latent optimisation for generative adversarial networks","author":"Wu","year":"2020"},{"key":"ref129","first-page":"8733","article-title":"Learning plannable representations with causal infoGAN","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Kurutach"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-71249-9_8"},{"key":"ref131","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.374"},{"key":"ref132","first-page":"1","article-title":"Generative adversarial text to image synthesis","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Reed"},{"key":"ref133","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00833"},{"key":"ref134","first-page":"217","article-title":"Learning what and where to draw","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Reed"},{"key":"ref135","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2856256"},{"key":"ref136","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.202"},{"issue":"5","key":"ref137","article-title":"Conditional generative adversarial nets for convolutional face generation","volume":"2014","author":"Gauthier","year":"2014","journal-title":"Class Project for Stanford CS231N: Convolutional Neural Netw. Vis. Recognit., Winter semester"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2017.8296650"},{"key":"ref139","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00252"},{"key":"ref140","article-title":"Learning to generate images of outdoor scenes from attributes and semantic layouts","author":"Karacan","year":"2016"},{"key":"ref141","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.323"},{"key":"ref142","first-page":"1887","article-title":"3D-aware scene manipulation via inverse graphics","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Yao"},{"key":"ref143","first-page":"1","article-title":"Robust conditional generative adversarial networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Chrysos"},{"key":"ref144","first-page":"10271","article-title":"Robustness of conditional GANs to noisy labels","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Thekumparampil"},{"key":"ref145","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00152"},{"key":"ref146","first-page":"1328","article-title":"Twin auxilary classifiers GAN","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Gong"},{"key":"ref147","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref148","first-page":"1","article-title":"Invertible conditional GANs for image editing","volume-title":"Proc. Conf. Neural Inf. Process. Syst. Workshop Adversarial Training","author":"Perarnau"},{"key":"ref149","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.308"},{"key":"ref150","article-title":"Semi-supervised conditional GANs","author":"Sricharan","year":"2017"},{"key":"ref151","first-page":"1","article-title":"cGANS with projection discriminator","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Miyato"},{"key":"ref152","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref153","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00917"},{"key":"ref154","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref155","first-page":"5495","article-title":"Alice: Towards understanding adversarial learning for joint distribution matching","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Li"},{"key":"ref156","first-page":"1","article-title":"Cycle-consistent adversarial learning as approximate Bayesian inference","volume-title":"Proc. Int. Conf. Mach. Learn. Workshop Theor. Found. Appl. Deep Generative Models","author":"Tiao"},{"key":"ref157","first-page":"1857","article-title":"Learning to discover cross-domain relations with generative adversarial networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kim"},{"key":"ref158","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.310"},{"key":"ref159","first-page":"1","article-title":"Towards principled methods for training generative adversarial networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Arjovsky"},{"key":"ref160","first-page":"1","article-title":"Stabilizing adversarial nets with prediction methods","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Yadav"},{"key":"ref161","first-page":"6626","article-title":"GANs trained by a two time-scale update rule converge to a local nash equilibrium","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Heusel"},{"key":"ref162","first-page":"700","article-title":"Are GANs created equal? A large-scale study","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Lucic"},{"key":"ref163","doi-asserted-by":"publisher","DOI":"10.1561\/0100000004"},{"key":"ref164","first-page":"1","article-title":"Quantitatively evaluating Gans with divergences proposed for training","volume-title":"Proc. Int. Conf. Learn. Representation","author":"Im"},{"key":"ref165","first-page":"1","article-title":"Generative adversarial nets from a density ratio estimation perspective","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Uehara"},{"key":"ref166","first-page":"1517","article-title":"Hilbert space embeddings and metrics on probability measures","volume":"11","author":"Sriperumbudur","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref167","first-page":"9086","article-title":"Nonparametric density estimation & convergence of GANs under besov IPM losses","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Uppal"},{"key":"ref168","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"Gretton","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref169","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btl242"},{"key":"ref170","first-page":"1","article-title":"Improving MMD-GAN training with repulsive loss function","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Wang"},{"key":"ref171","first-page":"6700","article-title":"On gradient regularizers for MMD GANs","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Arbel"},{"key":"ref172","article-title":"Demystifying MMD GANs","author":"Bi\u0144kowski","year":"2021"},{"key":"ref173","first-page":"2203","article-title":"MMD GAN: Towards deeper understanding of moment matching network","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Li"},{"key":"ref174","article-title":"Sobolev GAN","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Mroueh"},{"key":"ref175","article-title":"Generative models and model criticism via optimized maximum mean discrepancy","author":"Sutherland","year":"2021"},{"key":"ref176","first-page":"258","article-title":"Training generative neural networks via maximum mean discrepancy optimization","volume-title":"Proc. Conf. Uncertainty Artif. Intell.","author":"Dziugaite"},{"key":"ref177","first-page":"1718","article-title":"Generative moment matching networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li"},{"key":"ref178","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-71050-9"},{"key":"ref179","first-page":"2018","article-title":"Stabilizing training of generative adversarial networks through regularization","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Roth"},{"key":"ref180","first-page":"3481","article-title":"Which training methods for GANs do actually converge?","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Mescheder"},{"key":"ref181","first-page":"1","article-title":"Many paths to equilibrium: GANs do not need to decrease a divergence at every step","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Fedus"},{"key":"ref182","article-title":"On convergence and stability of GANs","author":"Kodali","year":"2017"},{"key":"ref183","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01228-1_40"},{"key":"ref184","article-title":"The cramer distance as a solution to biased wasserstein gradients","author":"Bellemare","year":"2017"},{"key":"ref185","first-page":"1","article-title":"On the regularization of wasserstein GANs","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Petzka"},{"key":"ref186","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2017-63"},{"key":"ref187","first-page":"6754","article-title":"Banach wasserstein GAN","volume-title":"Proc. Neural Info. Process. Syst.","author":"Adler"},{"key":"ref188","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00660"},{"key":"ref189","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2020.09.013"},{"key":"ref190","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref191","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref192","article-title":"The toronto face dataset","author":"Susskind","year":"2010"},{"key":"ref193","first-page":"1","article-title":"Striving for simplicity: The all convolutional net","volume-title":"Proc. Int. Conf. Learn. Representations Workshop","author":"Springenberg"},{"key":"ref194","article-title":"Progressive neural networks","author":"Rusu","year":"2016"},{"key":"ref195","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00143"},{"key":"ref196","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00467"},{"key":"ref197","volume-title":"Deep Learning","author":"Goodfellow","year":"2016"},{"key":"ref198","first-page":"3308","article-title":"VEEGAN: Reducing mode collapse in GANs using implicit variational learning","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Srivastava"},{"key":"ref199","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00460"},{"key":"ref200","first-page":"224","article-title":"Generalization and equilibrium in generative adversarial nets (GANs)","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Arora"},{"key":"ref201","first-page":"1825","article-title":"The numerics of GANs","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Mescheder"},{"key":"ref202","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2020.2983071"},{"key":"ref203","first-page":"1","article-title":"Do GANs learn the distribution? Some theory and empirics","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Arora"},{"key":"ref204","first-page":"1","article-title":"Approximability of discriminators implies diversity in GANs","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Bai"},{"key":"ref205","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3714011"},{"key":"ref206","first-page":"10246","article-title":"Nonparametric density estimation with adversarial losses","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Singh"},{"key":"ref207","article-title":"Learning in implicit generative models","author":"Mohamed","year":"2017"},{"key":"ref208","first-page":"1","article-title":"A variational inequality perspective on generative adversarial networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Gidel"},{"key":"ref209","first-page":"7091","article-title":"On the convergence and robustness of training GANs with regularized optimal transport","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Sanjabi"},{"key":"ref210","first-page":"1","article-title":"Theoretical insights into memorization in GANs","volume-title":"Proc. Neural Inf. Process. Syst. Workshop","author":"Nagarajan"},{"key":"ref211","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-11021-5_21"},{"key":"ref212","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46454-1_20"},{"key":"ref213","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/327"},{"key":"ref214","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01154-8"},{"key":"ref215","first-page":"1","article-title":"Amortised MAP inference for image super-resolution","volume-title":"Proc. Int. Conf. Learn. Representations","author":"S\u00f8nderby"},{"key":"ref216","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"ref217","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00070"},{"key":"ref218","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00319"},{"key":"ref219","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.141"},{"key":"ref220","first-page":"2867","article-title":"Learning a high fidelity pose invariant model for high-resolution face frontalization","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Cao"},{"key":"ref221","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00359"},{"key":"ref222","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/404"},{"key":"ref223","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.578"},{"key":"ref224","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00012"},{"key":"ref225","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00824"},{"key":"ref226","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01249-6_50"},{"key":"ref227","first-page":"1","article-title":"Semantically decomposing the latent spaces of generative adversarial networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Donahue"},{"key":"ref228","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8682970"},{"key":"ref229","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00125"},{"key":"ref230","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01249-6_40"},{"key":"ref231","first-page":"2670","article-title":"Dual variational generation for low shot heterogeneous face recognition","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Fu"},{"key":"ref232","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2019.2891116"},{"key":"ref233","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01215"},{"key":"ref234","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.299"},{"key":"ref235","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.608"},{"key":"ref236","first-page":"82","article-title":"Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Wu"},{"key":"ref237","article-title":"Generating images with recurrent adversarial networks","author":"Im","year":"2016"},{"key":"ref238","first-page":"1","article-title":"LR-GAN: Layered recursive generative adversarial networks for image generation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Yang"},{"key":"ref239","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.324"},{"key":"ref240","first-page":"1","article-title":"Decomposing motion and content for natural video sequence prediction","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Villegas"},{"key":"ref241","article-title":"Learning a driving simulator","author":"Santana","year":"2016"},{"key":"ref242","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00603"},{"key":"ref243","first-page":"1","article-title":"Deep multi-scale video prediction beyond mean square error","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Mathieu"},{"key":"ref244","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.194"},{"key":"ref245","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01228-1_8"},{"key":"ref246","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_34"},{"key":"ref247","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00986"},{"key":"ref248","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00901"},{"key":"ref249","doi-asserted-by":"publisher","DOI":"10.5244\/c.31.111"},{"key":"ref250","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3350944"},{"key":"ref251","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.606"},{"key":"ref252","article-title":"SalGAN: Visual saliency prediction with generative adversarial networks","author":"Pan","year":"2018"},{"key":"ref253","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00937"},{"key":"ref254","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107027"},{"key":"ref255","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2018.00127"},{"key":"ref256","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12317"},{"key":"ref257","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3007844"},{"key":"ref258","first-page":"1","article-title":"AlphaGAN: Generative adversarial networks for natural image matting","author":"Lutz","year":"2018","journal-title":"Proc. Brit. Mach. Vis. Conf."},{"key":"ref259","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.728"},{"key":"ref260","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00577"},{"key":"ref261","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2018.8451049"},{"key":"ref262","first-page":"1","article-title":"Large scale image completion via co-modulated generative adversarial networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhao"},{"key":"ref263","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073659"},{"key":"ref264","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2885799"},{"key":"ref265","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46604-0_55"},{"key":"ref266","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.377"},{"key":"ref267","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2871688"},{"key":"ref268","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2018.8462342"},{"key":"ref269","doi-asserted-by":"publisher","DOI":"10.23919\/EUSIPCO.2018.8553236"},{"key":"ref270","article-title":"Real-valued (medical) time series generation with recurrent conditional GANs","author":"Esteban","year":"2017"},{"key":"ref271","article-title":"Eeg-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals","author":"Hartmann","year":"2018"},{"key":"ref272","article-title":"Synthesizing audio with generative adversarial networks","author":"Donahue","year":"2018"},{"key":"ref273","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30490-4_56"},{"key":"ref274","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1230"},{"key":"ref275","first-page":"1","article-title":"Generating text via adversarial training","volume-title":"Proc. Conf. Neural Inf. Process. Syst. Workshop Adv. Training","author":"Zhang"},{"key":"ref276","first-page":"1","article-title":"MaskGAN: Better text generation via filling in the _","volume-title":"Proc. Int. Confe. Learn. Representations","author":"Fedus"},{"key":"ref277","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011238"},{"key":"ref278","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1133"},{"key":"ref279","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1083"},{"key":"ref280","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1046"},{"key":"ref281","first-page":"4302","article-title":"Training language GANs from scratch","volume-title":"Proc. Neural Inf. Process. Syst.","author":"d\u2019Autume"},{"key":"ref282","article-title":"TAC-GAN-text conditioned auxiliary classifier generative adversarial network","author":"Dash","year":"2017"},{"key":"ref283","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.64"},{"key":"ref284","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.445"},{"key":"ref285","first-page":"1","article-title":"Answer-based adversarial training for generating clarification questions","volume-title":"Proc. Annu. Conf. North Amer. Chapter Assoc. Comput. Linguistics","author":"Rao"},{"key":"ref286","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301395"},{"key":"ref287","doi-asserted-by":"publisher","DOI":"10.1145\/3240508.3240587"},{"key":"ref288","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357899"},{"key":"ref289","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1409"},{"key":"ref290","doi-asserted-by":"publisher","DOI":"10.1109\/SPW.2018.00022"},{"key":"ref291","first-page":"4910","article-title":"Generative adversarial network-based postfiltering for statistical parametric speech synthesis","volume-title":"Proc. IEEE Int. Conf. Acoust., Speech Signal Process.","author":"Takuhiro"},{"key":"ref292","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2017.2761547"},{"key":"ref293","first-page":"1","article-title":"Adversarial audio synthesis","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Donahue"},{"key":"ref294","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2017-1428"},{"key":"ref295","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2018.8462581"},{"key":"ref296","first-page":"1","article-title":"Generating and designing dna with deep generative models","volume-title":"Proc. Conf. Neural Inf. Process. Syst. Comput. Biol. Workshop","author":"Killoran"},{"key":"ref297","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0017-4"},{"key":"ref298","article-title":"Chemgan challenge for drug discovery: Can AI reproduce natural chemical diversity?","author":"Benhenda","year":"2017"},{"key":"ref299","first-page":"1","article-title":"Generating multi-label discrete patient records using generative adversarial networks","volume-title":"Proc. Mach. Learn. Healthcare","author":"Choi"},{"key":"ref300","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00889-5_30"},{"key":"ref301","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"ref302","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-68127-6_2"},{"key":"ref303","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2820120"},{"key":"ref304","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2858752"},{"key":"ref305","doi-asserted-by":"publisher","DOI":"10.1007\/s12021-018-9377-x"},{"key":"ref306","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2827462"},{"key":"ref307","doi-asserted-by":"publisher","DOI":"10.1109\/SMC.2018.00187"},{"key":"ref308","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-18590-9_63"},{"key":"ref309","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.405"},{"key":"ref310","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00028"},{"key":"ref311","doi-asserted-by":"publisher","DOI":"10.3389\/frobt.2018.00066"},{"key":"ref312","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8622525"},{"key":"ref313","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219850"},{"key":"ref314","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/201"},{"key":"ref315","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33018901"},{"key":"ref316","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.01040"},{"key":"ref317","first-page":"5361","article-title":"HyperGAN: A generative model for diverse, performant neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ratzlaff"},{"key":"ref318","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.09.013"},{"key":"ref319","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330873"},{"key":"ref320","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330972"},{"key":"ref321","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330866"},{"key":"ref322","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330970"},{"key":"ref323","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330869"},{"key":"ref324","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-19-8991-9_29"},{"key":"ref325","first-page":"1","article-title":"CycleGAN: A master of steganography","volume-title":"Proc. Confe. Neural Inf. Process. Syst. Workshop Mach. Deception","author":"Chu"},{"key":"ref326","doi-asserted-by":"publisher","DOI":"10.1117\/12.2559429"},{"key":"ref327","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-77380-3_51"},{"key":"ref328","first-page":"1954","article-title":"Generating steganographic images via adversarial training","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Hayes"},{"key":"ref329","article-title":"Learning to protect communications with adversarial neural cryptography","author":"Abadi","year":"2016"},{"key":"ref330","first-page":"1","article-title":"Unsupervised cipher cracking using discrete GANs","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Gomez"},{"key":"ref331","doi-asserted-by":"publisher","DOI":"10.1161\/circoutcomes.118.005122"},{"key":"ref332","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00240"},{"key":"ref333","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00333"},{"key":"ref334","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00290"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/69\/10063074\/09625798.pdf?arnumber=9625798","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:45:25Z","timestamp":1705020325000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9625798\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,1]]},"references-count":334,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2021.3130191","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"value":"1041-4347","type":"print"},{"value":"1558-2191","type":"electronic"},{"value":"2326-3865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,1]]}}}