{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T08:03:00Z","timestamp":1764403380807,"version":"3.37.3"},"reference-count":39,"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:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012389","name":"Institute of Information and Communications Technology Planning and Evaluation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012389","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Korean Government (MSIT) [Artificial Intelligence Convergence Innovation Human Resources Development (Inha University)]","award":["RS-2022-00155915"],"award-info":[{"award-number":["RS-2022-00155915"]}]},{"name":"AI Innovation Hub","award":["RS-2021-II212068"],"award-info":[{"award-number":["RS-2021-II212068"]}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Korean Government","award":["2022R1A2C2010095","2022R1A4A1033549"],"award-info":[{"award-number":["2022R1A2C2010095","2022R1A4A1033549"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3404071","type":"journal-article","created":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T17:42:36Z","timestamp":1716399756000},"page":"72860-72870","source":"Crossref","is-referenced-by-count":1,"title":["US-GAN: Ultrasound Image-Specific Feature Decomposition for Fine Texture Transfer"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5803-6629","authenticated-orcid":false,"given":"Seongho","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8742-3433","authenticated-orcid":false,"given":"Byung Cheol","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea"}]}],"member":"263","reference":[{"doi-asserted-by":"publisher","key":"ref1","DOI":"10.3390\/s17010149"},{"doi-asserted-by":"publisher","key":"ref2","DOI":"10.1016\/j.cmpb.2020.105709"},{"doi-asserted-by":"publisher","key":"ref3","DOI":"10.1007\/s00521-021-06851-5"},{"doi-asserted-by":"publisher","key":"ref4","DOI":"10.1007\/s11548-016-1513-1"},{"doi-asserted-by":"publisher","key":"ref5","DOI":"10.1016\/j.ultras.2018.07.006"},{"doi-asserted-by":"publisher","key":"ref6","DOI":"10.1007\/s00521-022-07719-y"},{"doi-asserted-by":"publisher","key":"ref7","DOI":"10.1109\/ACCESS.2020.3000666"},{"doi-asserted-by":"publisher","key":"ref8","DOI":"10.1007\/978-3-030-87237-3_63"},{"doi-asserted-by":"publisher","key":"ref9","DOI":"10.1007\/s00521-020-05687-9"},{"doi-asserted-by":"publisher","key":"ref10","DOI":"10.1109\/ICCV.2017.244"},{"doi-asserted-by":"publisher","key":"ref11","DOI":"10.1007\/978-3-030-58545-7_19"},{"doi-asserted-by":"publisher","key":"ref12","DOI":"10.1109\/CVPR.2016.265"},{"key":"ref13","first-page":"1","article-title":"Infogan: Interpretable representation learning by information maximizing generative adversarial nets","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"29","author":"Chen"},{"key":"ref14","first-page":"1","article-title":"Beta-vae: Learning basic visual concepts with a constrained variational framework","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Higgins"},{"doi-asserted-by":"publisher","key":"ref15","DOI":"10.1007\/978-3-030-01246-5_3"},{"doi-asserted-by":"publisher","key":"ref16","DOI":"10.1109\/CVPR52688.2022.01775"},{"doi-asserted-by":"publisher","key":"ref17","DOI":"10.1109\/CVPR.2017.632"},{"doi-asserted-by":"publisher","key":"ref18","DOI":"10.1109\/CVPR.2018.00917"},{"doi-asserted-by":"publisher","key":"ref19","DOI":"10.1109\/WACV56688.2023.00081"},{"key":"ref20","first-page":"1","article-title":"Toward multimodal image-to-image translation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Zhu"},{"doi-asserted-by":"publisher","key":"ref21","DOI":"10.1109\/CVPR46437.2021.01614"},{"doi-asserted-by":"publisher","key":"ref22","DOI":"10.1109\/CVPR.2019.00505"},{"key":"ref23","first-page":"6767","article-title":"Blockgan: Learning 3D object-aware scene representations from unlabelled images","volume":"33","author":"Nguyen-Phuoc","year":"2020","journal-title":"Adv. neural Inf. Process. Syst."},{"doi-asserted-by":"publisher","key":"ref24","DOI":"10.1109\/ICCV48922.2021.00911"},{"doi-asserted-by":"publisher","key":"ref25","DOI":"10.1109\/CVPR.2018.00165"},{"key":"ref26","article-title":"Representation learning with contrastive predictive coding","author":"van den Oord","year":"2018","journal-title":"arXiv:1807.03748"},{"doi-asserted-by":"publisher","key":"ref27","DOI":"10.5555\/3524938.3525087"},{"doi-asserted-by":"publisher","key":"ref28","DOI":"10.5555\/3495724.3497510"},{"doi-asserted-by":"publisher","key":"ref29","DOI":"10.48550\/ARXIV.1706.03762"},{"doi-asserted-by":"publisher","key":"ref30","DOI":"10.1002\/j.1538-7305.1948.tb01338.x"},{"doi-asserted-by":"publisher","key":"ref31","DOI":"10.1109\/ICCV.2017.304"},{"key":"ref32","article-title":"Patch-based image inpainting with generative adversarial networks","author":"Demir","year":"2018","journal-title":"arXiv:1803.07422"},{"doi-asserted-by":"publisher","key":"ref33","DOI":"10.1109\/TMI.2014.2377694"},{"key":"ref34","first-page":"1","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Paszke"},{"key":"ref35","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. 3rd Int. Conf. Learn. Represent.","author":"Kingma"},{"key":"ref36","first-page":"1","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.","volume":"30","author":"Heusel"},{"doi-asserted-by":"publisher","key":"ref37","DOI":"10.1109\/CVPR.2016.308"},{"doi-asserted-by":"publisher","key":"ref38","DOI":"10.1109\/CVPR.2009.5206848"},{"doi-asserted-by":"publisher","key":"ref39","DOI":"10.1364\/JOSAA.7.002032"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10380310\/10536722.pdf?arnumber=10536722","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T04:28:08Z","timestamp":1716870488000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10536722\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":39,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3404071","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2024]]}}}