{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T23:38:34Z","timestamp":1778888314788,"version":"3.51.4"},"reference-count":58,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["OAC-1934757"],"award-info":[{"award-number":["OAC-1934757"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006751","name":"U.S. Army","doi-asserted-by":"publisher","award":["W911NF-15-1-0479"],"award-info":[{"award-number":["W911NF-15-1-0479"]}],"id":[{"id":"10.13039\/100006751","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Signal Process. Mag."],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1109\/msp.2022.3215282","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T18:55:44Z","timestamp":1672685744000},"page":"148-163","source":"Crossref","is-referenced-by-count":25,"title":["Generative Models for Inverse Imaging Problems: From mathematical foundations to physics-driven applications"],"prefix":"10.1109","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3594-5840","authenticated-orcid":false,"given":"Zhizhen","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering and Coordinated Science Laboratory, University of Illinois at Urbana-Champaign (UIUC), Urbana, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9763-9609","authenticated-orcid":false,"given":"Jong Chul","family":"Ye","sequence":"additional","affiliation":[{"name":"Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST), Daejon, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9738-1094","authenticated-orcid":false,"given":"Yoram","family":"Bresler","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering and Coordinated Science Laboratory, University of Illinois at Urbana-Champaign (UIUC), Urbana, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Solving inverse problems in medical imaging with score-based generative models","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Song","year":"2022"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102479"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1137\/20m1317992"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/tci.2021.3096491"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/tci.2022.3197939"},{"key":"ref6","article-title":"Auto-encoding variational Bayes","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Kingma","year":"2013"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref8","first-page":"214","article-title":"Wasserstein generative adversarial networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","volume":"70","author":"Arjovsky","year":"2017"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/tci.2020.3006735"},{"key":"ref10","first-page":"1530","article-title":"Variational inference with normalizing flows","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Rezende","year":"2015"},{"key":"ref11","first-page":"11,918","article-title":"Generative modeling by estimating gradients of the data distribution","volume-title":"Proc. 33rd Adv. Neural Inf. Process. Syst.","volume":"32","author":"Song","year":"2019"},{"key":"ref12","first-page":"3608","article-title":"Implicit generation and modeling with energy-based models","volume-title":"Proc. 33rd Adv. Neural Inf. Process. Syst.","volume":"32","author":"Du","year":"2019"},{"key":"ref13","article-title":"Denoising diffusion implicit models","volume-title":"Proc. 9th Int. Conf. Learn. Represent. (ICLR)","author":"Song","year":"2021"},{"key":"ref14","article-title":"Score-based generative modeling through stochastic differential equations","volume-title":"Proc. 9th Int. Conf. Learn. Represent. (ICLR)","author":"Song","year":"2021"},{"key":"ref15","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Ho","year":"2020"},{"key":"ref16","first-page":"537","article-title":"Compressed sensing using generative models","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","volume":"70","author":"Bora","year":"2017"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/tci.2020.3032671"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-66415-2_28"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/isbi48211.2021.9433970"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/msp.2021.3119273"},{"key":"ref21","first-page":"4743","article-title":"Improved variational inference with inverse autoregressive flow","volume-title":"Proc. 30th Int. Conf. Neural Inf. Process. Syst.","volume":"29","author":"Kingma","year":"2016"},{"key":"ref22","article-title":"Importance weighted autoencoders","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Burda","year":"2016"},{"key":"ref23","first-page":"271","article-title":"f-GAN: Training generative neural samplers using variational divergence minimization","volume-title":"Proc. 30th Int. Conf. Neural Inf. Process. Syst.","volume":"29","author":"Nowozin","year":"2016"},{"key":"ref24","first-page":"5769","article-title":"Improved training of Wasserstein GANs","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","volume":"30","author":"Gulrajani","year":"2017"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr42600.2020.00813"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2021.3116668"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2017.195"},{"key":"ref28","article-title":"Towards faster and stabilized GAN training for high-fidelity few-shot image synthesis","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Liu","year":"2021"},{"issue":"4","key":"ref29","first-page":"695","article-title":"Estimation of non-normalized statistical models by score matching","volume":"6","author":"Hyvrinen","year":"2005","journal-title":"J. Mach. Learn. Res."},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1162\/neco_a_00142"},{"key":"ref31","first-page":"574","article-title":"Sliced score matching: A scalable approach to density and score estimation","volume-title":"Proc. Uncertainty Artif. Intell.","author":"Song","year":"2020"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/0304-4149(82)90051-5"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/0550-3213(81)90056-0"},{"key":"ref34","first-page":"6,572","article-title":"Neural ordinary differential equations","volume-title":"Proc. 32nd Conf. Neural Inf. Process. Syst.","volume":"31","author":"Chen","year":"2018"},{"key":"ref35","article-title":"FFJORD: Free-form continuous dynamics for scalable reversible generative models","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Grathwohl","year":"2019"},{"key":"ref36","first-page":"11,287","article-title":"Score-based generative modeling in latent space","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Vahdat","year":"2021"},{"key":"ref37","article-title":"Reconstructing continuous distributions of 3D protein structure from cryo-EM images","volume-title":"Proc. Int. Comput. Learn. Represent. (ICLR)","author":"Zhong","year":"2020"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/iccv.2017.244"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/tsp.2020.2977256"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2021.3084288"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2021.3065948"},{"key":"ref42","first-page":"14,938","article-title":"Robust compressed sensing MRI with deep generative priors","volume-title":"Proc. 35th Conf. Neural Inf. Process. Syst.","volume":"34","author":"Jalal","year":"2021"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsb.2012.09.006"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.4169"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1419276111"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6420\/ab4f55"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-01049-4"},{"key":"ref48","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Kingma","year":"2015"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/iccv48922.2021.00403"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.21105\/joss.00861"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2016.11.020"},{"key":"ref52","article-title":"Categorical reparameterization with Gumbel-softmax","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Jang","year":"2016"},{"key":"ref53","article-title":"Solving linear inverse problems using the prior implicit in a denoiser","volume-title":"Proc. 2020 Workshop Deep Learn. Inverse Problems (NeurIPS)","author":"Kadkhodaie"},{"key":"ref54","first-page":"21,757","article-title":"SNIPS: Solving noisy inverse problems stochastically","volume-title":"Proc. 35th Conf. Neural Inf. Process. Syst.","volume":"34","author":"Kawar","year":"2021"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52688.2022.01209"},{"key":"ref56","first-page":"5072","article-title":"Subspace robust Wasserstein distances","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Paty","year":"2019"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2018.00367"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2019.01090"}],"container-title":["IEEE Signal Processing Magazine"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/79\/10004746\/10004774-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/79\/10004746\/10004774.pdf?arnumber=10004774","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T02:50:12Z","timestamp":1707447012000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10004774\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":58,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/msp.2022.3215282","relation":{},"ISSN":["1053-5888","1558-0792"],"issn-type":[{"value":"1053-5888","type":"print"},{"value":"1558-0792","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1]]}}}