{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T20:20:57Z","timestamp":1740169257432,"version":"3.37.3"},"reference-count":60,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea (NRF) funded by the Korea Government, Ministry of Science and ICT","doi-asserted-by":"publisher","award":["2018R1A5A1059921","2019R1C1C1009192","2021R1F1A106153511"],"award-info":[{"award-number":["2018R1A5A1059921","2019R1C1C1009192","2021R1F1A106153511"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010418","name":"Institute of Information & Communications Technology Planning & Evaluation (IITP) funded by the Korea Government","doi-asserted-by":"publisher","award":["2019-0-00075"],"award-info":[{"award-number":["2019-0-00075"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007107","name":"Artificial Intelligence Graduate School Program [Korea Advanced Institute of Science and Technology (KAIST)]","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100007107","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Korea Medical Device Development Fund funded by the Korea Government","award":["202011B08-02","KMDF_PR_20200901_0014-2021-02"],"award-info":[{"award-number":["202011B08-02","KMDF_PR_20200901_0014-2021-02"]}]},{"DOI":"10.13039\/100004358","name":"Future Medicine 20*30 Project of the Samsung Medical Center","doi-asserted-by":"publisher","award":["SMX1210791"],"award-info":[{"award-number":["SMX1210791"]}],"id":[{"id":"10.13039\/100004358","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3128721","type":"journal-article","created":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T22:54:45Z","timestamp":1637189685000},"page":"155335-155352","source":"Crossref","is-referenced-by-count":1,"title":["Compressed Sensing via Measurement-Conditional Generative Models"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6622-6545","authenticated-orcid":false,"given":"Kyung-Su","family":"Kim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jung Hyun","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eunho","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5708"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.2970707"},{"key":"ref33","article-title":"Conditional generative adversarial nets","author":"mirza","year":"2014","journal-title":"arXiv 1411 1784"},{"key":"ref32","article-title":"Deep decoder: Concise image representations from untrained non-convolutional networks","author":"heckel","year":"2019","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00984"},{"key":"ref30","first-page":"14832","article-title":"Algorithmic guarantees for inverse imaging with untrained network priors","author":"jagatap","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref37","article-title":"Phase retrieval using conditional generative adversarial networks","author":"uelwer","year":"2019","journal-title":"arXiv 1912 04981"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00917"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref34","first-page":"1060","article-title":"Generative adversarial text to image synthesis","author":"reed","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/S1874-5849(01)80010-3"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2016\/07\/073401"},{"key":"ref27","first-page":"9061","article-title":"Theoretical linear convergence of unfolded ISTA and its practical weights and thresholds","author":"chen","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2017.2708040"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2020.102680"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2018.2832128"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00570"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2018.8462233"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"ref24","first-page":"399","article-title":"Learning fast approximations of sparse coding","author":"gregor","year":"2010","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref23","article-title":"Task-aware compressed sensing with generative adversarial networks","volume":"32","author":"kabkab","year":"2018","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2018.2791945"},{"key":"ref25","article-title":"Understanding neural sparse coding with matrix factorization","author":"moreau","year":"2017","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref50","first-page":"1","article-title":"Auto-encoding variational Bayes","author":"kingma","year":"2014","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref51","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref59","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume":"70","author":"finn","year":"2017","journal-title":"Proc 34th Int Conf Mach Learn"},{"key":"ref58","article-title":"fastMRI: An open dataset and benchmarks for accelerated MRI","author":"zbontar","year":"2018","journal-title":"arXiv 1811 08839"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1126\/science.aab3050"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2012.2189196"},{"key":"ref52","article-title":"BEGAN: Boundary equilibrium generative adversarial networks","author":"berthelot","year":"2017","journal-title":"arXiv 1703 10717"},{"key":"ref10","first-page":"5554","article-title":"From Bayesian sparsity to gated recurrent nets","author":"he","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref11","first-page":"1772","article-title":"Learned D-AMP: Principled neural network based compressive image recovery","author":"metzler","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref40","first-page":"399","article-title":"Invertible generative models for inverse problems: Mitigating representation error and dataset bias","author":"asim","year":"2020","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref12","article-title":"Compressed sensing with deep image prior and learned regularization","author":"van veen","year":"2018","journal-title":"arXiv 1806 06438"},{"key":"ref13","first-page":"1222","article-title":"Modeling sparse deviations for compressed sensing using generative models","author":"dhar","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref14","first-page":"9573","article-title":"Neural proximal gradient descent for compressive imaging","author":"mardani","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref15","first-page":"8507","article-title":"Adversarial regularizers in inverse problems","author":"lunz","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref16","first-page":"1","article-title":"A data-driven and distributed approach to sparse signal representation and recovery","author":"mousavi","year":"2019","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref17","first-page":"2514","article-title":"Uncertainty autoencoders: Learning compressed representations via variational information maximization","author":"grover","year":"2019","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref18","first-page":"6828","article-title":"Learning a compressed sensing measurement matrix via gradient unrolling","volume":"97","author":"wu","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref19","first-page":"6850","article-title":"Deep compressed sensing","author":"wu","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref4","first-page":"10","article-title":"Deep ADMM-Net for compressive sensing MRI","author":"yang","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2007.914728"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.23919\/EUSIPCO.2019.8903056"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1364\/FIO.2011.FMM1"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1177\/1550147720908748"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.3026467"},{"key":"ref49","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"radford","year":"2015","journal-title":"arXiv 1511 06434"},{"key":"ref9","first-page":"537","article-title":"Compressed sensing using generative models","author":"bora","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1117\/12.2527753"},{"key":"ref45","article-title":"Progressively volumetrized deep generative models for data-efficient contextual learning of MR image recovery","author":"yurt","year":"2020","journal-title":"arXiv 2011 13913"},{"key":"ref48","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2858752"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2020.3001737"},{"key":"ref41","article-title":"Unsupervised MRI reconstruction via zero-shot learned adversarial transformers","author":"korkmaz","year":"2021","journal-title":"arXiv 2105 08059"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11010061"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1117\/12.2581004"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09617735.pdf?arnumber=9617735","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,8]],"date-time":"2022-03-08T21:51:22Z","timestamp":1646776282000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9617735\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":60,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3128721","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2021]]}}}