{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,2]],"date-time":"2025-05-02T11:36:42Z","timestamp":1746185802558,"version":"3.37.3"},"reference-count":32,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2021ZD0112001"],"award-info":[{"award-number":["2021ZD0112001"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62171111"],"award-info":[{"award-number":["62171111"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Signal Process. Lett."],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/lsp.2023.3337711","type":"journal-article","created":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T19:35:24Z","timestamp":1701372924000},"page":"106-110","source":"Crossref","is-referenced-by-count":3,"title":["Closed-Loop Training for Projected GAN"],"prefix":"10.1109","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6801-2451","authenticated-orcid":false,"given":"Jiangwei","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2462-5160","authenticated-orcid":false,"given":"Liang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3903-071X","authenticated-orcid":false,"given":"Lili","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7481-095X","authenticated-orcid":false,"given":"Hongliang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2019.2903874"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2022.3207621"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2020.3040656"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2020.3003828"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2018.2809685"},{"key":"ref7","first-page":"12104","article-title":"Training generative adversarial networks with limited data","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Karras","year":"2020"},{"key":"ref8","first-page":"17480","article-title":"Projected GANs converge faster","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Sauer","year":"2021"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.265"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2916751"},{"key":"ref11","article-title":"Towards faster and stabilized GAN training for high-fidelity few-shot image synthesis","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Liu","year":"2020"},{"key":"ref12","first-page":"7559","article-title":"Differentiable augmentation for data-efficient GAN training","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhao","year":"2020"},{"key":"ref13","first-page":"33387","article-title":"FreGAN: Exploiting frequency components for training GANs under limited data","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Wang","year":"2022"},{"key":"ref14","first-page":"20941","article-title":"Data-efficient GAN training beyond (just) augmentations: A lottery ticket perspective","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Chen","year":"2021"},{"key":"ref15","article-title":"Freeze the discriminator: A simple baseline for fine-tuning GANs","volume-title":"Proc. CVPR AI Content Creation Workshop","author":"Mo","year":"2020"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref17","first-page":"10566","article-title":"Understanding and stabilizing GANs training dynamics using control theory","volume-title":"Proc. Int. Conf. Mach. Learn","author":"Xu","year":"2020"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/tcyb.2023.3267773"},{"key":"ref19","first-page":"23140","article-title":"Stabilizing GANs training with brownian motion controller","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"202","author":"Luo","year":"2023"},{"key":"ref20","volume-title":"Linear Systems","volume":"156","author":"Kailath","year":"1980"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1515\/9781400876457"},{"volume-title":"A Treatise on the Stability of a Given State of Motion","year":"1877","author":"Routh","key":"ref22"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/BF01446812"},{"year":"2023","key":"ref24","article-title":"Pokemon."},{"year":"2023","key":"ref25","article-title":"Art-Paint."},{"year":"2023","key":"ref26","article-title":"Landscape"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"ref28","article-title":"GANs trained by a two time-scale update rule converge to a local nash equilibrium","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Heusel","year":"2017"},{"key":"ref29","first-page":"214","article-title":"Wasserstein generative adversarial networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Arjovsky","year":"2017"},{"article-title":"Began: Boundary equilibrium generative adversarial networks","year":"2017","author":"Berthelot","key":"ref30"},{"key":"ref31","first-page":"6105","article-title":"Efficientnet: Rethinking model scaling for convolutional neural networks","volume-title":"Proc. Int. Conf. Mach. Learn","author":"Tan","year":"2019"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00813"}],"container-title":["IEEE Signal Processing Letters"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/97\/10380231\/10334000.pdf?arnumber=10334000","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,3]],"date-time":"2024-03-03T04:17:28Z","timestamp":1709439448000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10334000\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":32,"URL":"https:\/\/doi.org\/10.1109\/lsp.2023.3337711","relation":{},"ISSN":["1070-9908","1558-2361"],"issn-type":[{"type":"print","value":"1070-9908"},{"type":"electronic","value":"1558-2361"}],"subject":[],"published":{"date-parts":[[2024]]}}}