乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T07:12:27Z","timestamp":1723187547427},"reference-count":58,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"NSF","award":["DMS-1854791","OAC-1934757"]},{"DOI":"10.13039\/100000879","name":"Alfred P. Sloan Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000879","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Comput. Imaging"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/tci.2022.3197939","type":"journal-article","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T19:29:03Z","timestamp":1660159743000},"page":"705-720","source":"Crossref","is-referenced-by-count":5,"title":["An Adversarial Learning Based Approach for 2D Unknown View Tomography"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-0958-356X","authenticated-orcid":false,"given":"Mona","family":"Zehni","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering and Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3594-5840","authenticated-orcid":false,"given":"Zhizhen","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering and Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780195182187.001.0001"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TASSP.1981.1163528"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/53\/17\/021"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/59\/12\/2997"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13123"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2019.06.031"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1038\/nature25988"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.21037\/qims.2019.12.12"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6420\/aa9581"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2799231"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1107\/S1600577520000831"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2713099"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2715284"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0057-9"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2823768"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2823756"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2827462"},{"key":"ref18","article-title":"Adversarial Regularizers in Inverse Problems","volume-title":"Advances in Neural Information Processing Systems","volume":"31","author":"Lunz ktem","year":"2018"},{"key":"ref19","first-page":"21413","article-title":"End-to-end reconstruction meets data-driven regularization for inverse problems","volume-title":"Advances in Neural Information Processing Systems","volume":"34","author":"Mukherjee","year":"2021"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/83.846251"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/83.846252"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2342-10-12"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2008.2002305"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1137\/090764657"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s10851-016-0673-5"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2016.11.023"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8682401"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053170"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2018.8363873"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/icip.2019.8803755"},{"key":"ref31","first-page":"2672","article-title":"Generative Adversarial Nets","volume-title":"Advances in Neural Information Processing Systems","volume":"27","author":"Goodfellow","year":"2014"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2021.3096491"},{"key":"ref33","first-page":"214","article-title":"Wasserstein generative adversarial networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","volume":"70","author":"Arjovsky","year":"2017"},{"key":"ref34","article-title":"Categorical reparameterization with Gumbel-softmax","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Jang","year":"2017"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9414895"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI48211.2021.9433970"},{"key":"ref37","first-page":"5769","article-title":"Improved training of Wasserstein GANs","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Gulrajani","year":"2017"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1137\/S003614450343200X"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2016.2514700"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1364\/JOSAA.30.000871"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1038\/238435a0"},{"key":"ref42","article-title":"Spectral normalization for generative adversarial networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Miyato","year":"2018"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2627573"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1137\/16M1097171"},{"key":"ref45","article-title":"An introduction to the conjugate gradient method without the agonizing pain","author":"Shewchuk","year":"1994"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsb.2012.09.006"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14594"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1038\/s41564-018-0237-0"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1002\/jcc.20084"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1016\/j.acha.2005.11.003"},{"key":"ref51","article-title":"Empirical evaluation of rectified activations in convolutional network","author":"Xu","year":"2015"},{"key":"ref52","article-title":"AmbientGAN: Generative models from lossy measurements","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Bora","year":"2018"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1364\/OE.24.025129"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898719284"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1561\/2200000016"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6420\/ab2ae9"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1038\/nature14427"},{"key":"ref58","article-title":"Tomographic X-ray data of a walnut","author":"Hmlinen","year":"2015"}],"container-title":["IEEE Transactions on Computational Imaging"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/6745852\/9679468\/9854178-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6745852\/9679468\/09854178.pdf?arnumber=9854178","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T05:01:18Z","timestamp":1709355678000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9854178\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":58,"URL":"https:\/\/doi.org\/10.1109\/tci.2022.3197939","relation":{},"ISSN":["2333-9403","2334-0118","2573-0436"],"issn-type":[{"value":"2333-9403","type":"electronic"},{"value":"2334-0118","type":"electronic"},{"value":"2573-0436","type":"print"}],"subject":[],"published":{"date-parts":[[2022]]}}}