{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T07:56:27Z","timestamp":1758268587549,"version":"3.37.3"},"reference-count":66,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":["U22A20102","62272419"],"award-info":[{"award-number":["U22A20102","62272419"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["ZJNSFLZ22F020010"],"award-info":[{"award-number":["ZJNSFLZ22F020010"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Circuits Syst. Video Technol."],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1109\/tcsvt.2024.3456476","type":"journal-article","created":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T18:45:49Z","timestamp":1726080349000},"page":"670-683","source":"Crossref","is-referenced-by-count":1,"title":["Nested Annealed Training Scheme for Generative Adversarial Networks"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-2899-3560","authenticated-orcid":false,"given":"Chang","family":"Wan","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Normal University, Jinhua, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4848-2304","authenticated-orcid":false,"given":"Ming-Hsuan","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of California at Merced, Merced, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6547-3472","authenticated-orcid":false,"given":"Minglu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Normal University, Jinhua, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4500-5836","authenticated-orcid":false,"given":"Yunliang","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Normal University, Jinhua, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5271-9215","authenticated-orcid":false,"given":"Zhonglong","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Normal University, Jinhua, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"2371","article-title":"Composite functional gradient learning of generative adversarial models","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Johnson"},{"key":"ref2","article-title":"Imagen video: High definition video generation with diffusion models","author":"Ho","year":"2022","journal-title":"arXiv:2210.02303"},{"key":"ref3","first-page":"8821","article-title":"Zero-shot text-to-image generation","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Ramesh"},{"key":"ref4","article-title":"Hierarchical text-conditional image generation with CLIP latents","author":"Ramesh","year":"2022","journal-title":"arXiv:2204.06125"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3588432.3591500"},{"key":"ref6","article-title":"LDM3D: Latent diffusion model for 3D","author":"Ben Melech Stan","year":"2023","journal-title":"arXiv:2305.10853"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3592103"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00985"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3348471"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3235410"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3222456"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2924428"},{"key":"ref14","first-page":"11918","article-title":"Generative modeling by estimating gradients of the data distribution","volume-title":"Proc. Conf. Neural Inf. Process. Syst.","volume":"32","author":"Song"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008923215028"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1126\/science.220.4598.671"},{"article-title":"Le arning multiple layers of features from tiny images","year":"2009","author":"Krizhevsky","key":"ref17"},{"key":"ref18","article-title":"LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop","author":"Yu","year":"2015","journal-title":"arXiv:1506.03365"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref21","article-title":"Improved training of Wasserstein GANs","author":"Gulrajani","year":"2017","journal-title":"arXiv:1704.00028"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.304"},{"key":"ref23","article-title":"Geometric GAN","author":"Hyun Lim","year":"2017","journal-title":"arXiv:1705.02894"},{"key":"ref24","article-title":"Large scale GAN training for high fidelity natural image synthesis","author":"Brock","year":"2018","journal-title":"arXiv:1809.11096"},{"key":"ref25","article-title":"Tackling the generative learning trilemma with denoising diffusion GANs","author":"Xiao","year":"2021","journal-title":"arXiv:2112.07804"},{"key":"ref26","first-page":"2234","article-title":"Improved techniques for training GANs","volume-title":"Proc. 30th Int. Conf. Neural Inf. Process. Syst.","author":"Salimans"},{"key":"ref27","first-page":"6629","article-title":"GANs trained by a two time-scale update rule converge to a local Nash equilibrium","volume-title":"Proc. Conf. Neural Inf. Process. Syst.","volume":"30","author":"Heusel"},{"key":"ref28","first-page":"12438","article-title":"Improved techniques for training score-based generative models","volume-title":"Proc. Conf. Neural Inf. Process. Syst.","volume":"33","author":"Song"},{"key":"ref29","first-page":"2256","article-title":"Deep unsupervised learning using nonequilibrium thermodynamics","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Sohl-Dickstein"},{"key":"ref30","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. Conf. Neural Inf. Process. Syst.","volume":"33","author":"Ho"},{"key":"ref31","article-title":"Score-based generative modeling through stochastic differential equations","author":"Song","year":"2020","journal-title":"arXiv:2011.13456"},{"key":"ref32","article-title":"Analytic-DPM: An analytic estimate of the optimal reverse variance in diffusion probabilistic models","author":"Bao","year":"2022","journal-title":"arXiv:2201.06503"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref34","first-page":"36479","article-title":"Photorealistic text-to-image diffusion models with deep language understanding","volume-title":"Proc. NIPS","volume":"35","author":"Saharia"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3326293"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2024.3359238"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10395-6"},{"key":"ref38","article-title":"Training generative adversarial networks via stochastic Nash games","author":"Franci","year":"2020","journal-title":"arXiv:2010.10013"},{"key":"ref39","article-title":"Stackelberg GAN: Towards provable minimax equilibrium via multi-generator architectures","author":"Zhang","year":"2018","journal-title":"arXiv:1811.08010"},{"key":"ref40","first-page":"2810","article-title":"Finding mixed Nash equilibria of generative adversarial networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Hsieh"},{"key":"ref41","article-title":"FedGAN: Federated generative adversarial networks for distributed data","author":"Rasouli","year":"2020","journal-title":"arXiv:2006.07228"},{"key":"ref42","first-page":"2667","article-title":"Dual discriminator generative adversarial nets","volume-title":"Proc. Conf. Neural Inf. Process. Syst.","volume":"30","author":"Nguyen"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.3022695"},{"key":"ref44","first-page":"4091","article-title":"Triple generative adversarial nets","volume-title":"Proc. Conf. Neural Inf. Process. Syst.","volume":"30","author":"Li"},{"key":"ref45","article-title":"Many paths to equilibrium: GANs do not need to decrease a divergence at every step","author":"Fedus","year":"2017","journal-title":"arXiv:1710.08446"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1703.10717"},{"key":"ref47","article-title":"An online learning approach to generative adversarial networks","author":"Grnarova","year":"2017","journal-title":"arXiv:1706.03269"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_8"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2018.8545049"},{"key":"ref50","article-title":"Mode regularized generative adversarial networks","author":"Che","year":"2016","journal-title":"arXiv:1612.02136"},{"key":"ref51","article-title":"Smoothness and stability in GANs","author":"Chu","year":"2020","journal-title":"arXiv:2002.04185"},{"key":"ref52","article-title":"Gradient descent GAN optimization is locally stable","author":"Nagarajan","year":"2017","journal-title":"arXiv:1706.04156"},{"key":"ref53","article-title":"Stabilizing training of generative adversarial networks through regularization","author":"Roth","year":"2017","journal-title":"arXiv:1705.09367"},{"key":"ref54","first-page":"3481","article-title":"Which training methods for gans do actually converge?","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Mescheder"},{"key":"ref55","first-page":"214","article-title":"Wasserstein generative adversarial networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Arjovsky"},{"key":"ref56","first-page":"5529","article-title":"Hierarchical implicit models and likelihood-free variational inference","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"2017","author":"Tran"},{"key":"ref57","first-page":"271","article-title":"F-GAN: Training generative neural samplers using variational divergence minimization","volume-title":"Proc. NIPS","author":"Nowozin"},{"key":"ref58","first-page":"14434","article-title":"Variational inference via Wasserstein gradient flows","volume-title":"Proc. Conf. Neural Inf. Process. Syst.","volume":"35","author":"Lambert"},{"key":"ref59","article-title":"Variational Wasserstein gradient flow","author":"Fan","year":"2021","journal-title":"arXiv:2112.02424"},{"key":"ref60","first-page":"2093","article-title":"Deep generative learning via variational gradient flow","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Gao"},{"key":"ref61","first-page":"1008","article-title":"Gradient layer: Enhancing the convergence of adversarial training for generative models","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Nitanda"},{"issue":"228","key":"ref62","first-page":"1","article-title":"How well generative adversarial networks learn distributions","volume":"22","author":"Liang","year":"2021","journal-title":"J. Mach. Learn. Res."},{"issue":"116","key":"ref63","first-page":"1","article-title":"An error analysis of generative adversarial networks for learning distributions","volume":"23","author":"Huang","year":"2022","journal-title":"J. Mach. Learn. Res."},{"key":"ref64","article-title":"Error analysis of generative adversarial network","author":"Hasan","year":"2023","journal-title":"arXiv:2310.15387"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00336"},{"key":"ref66","article-title":"The numerics of GANs","author":"Mescheder","year":"2017","journal-title":"arXiv:1705.10461"}],"container-title":["IEEE Transactions on Circuits and Systems for Video Technology"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/76\/10857816\/10677357.pdf?arnumber=10677357","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T05:46:48Z","timestamp":1738216008000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10677357\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":66,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tcsvt.2024.3456476","relation":{},"ISSN":["1051-8215","1558-2205"],"issn-type":[{"type":"print","value":"1051-8215"},{"type":"electronic","value":"1558-2205"}],"subject":[],"published":{"date-parts":[[2025,1]]}}}