{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T08:21:21Z","timestamp":1758874881807,"version":"3.37.3"},"reference-count":45,"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"}],"funder":[{"DOI":"10.13039\/100004358","name":"Samsung Research Funding Incubation Center of Samsung Electronics under Project Number","doi-asserted-by":"publisher","award":["SRFC-IT2002-05"],"award-info":[{"award-number":["SRFC-IT2002-05"]}],"id":[{"id":"10.13039\/100004358","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Chung-Ang University Graduate Research Scholarship in 2020"},{"DOI":"10.13039\/501100003661","name":"Korea Institute for Advancement of Technology (KIAT) grant","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003621","name":"Korea Government (MOTIE, Ministry of Trade, Industry and Energy)","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/access.2022.3161622","type":"journal-article","created":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T19:30:29Z","timestamp":1648063829000},"page":"32889-32899","source":"Crossref","is-referenced-by-count":2,"title":["Resource-Efficient Multi-Task Deep Learning Using a Multi-Path Network"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1009-8630","authenticated-orcid":false,"given":"Soyeon","family":"Park","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Chung-Ang University, Seoul, South Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2516-1380","authenticated-orcid":false,"given":"Jiho","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Chung-Ang University, Seoul, South Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0840-0044","authenticated-orcid":false,"given":"Eunwoo","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Chung-Ang University, Seoul, South Korea"}]}],"member":"263","reference":[{"doi-asserted-by":"publisher","key":"ref1","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref2","article-title":"Attention is all you need","author":"Vaswani","year":"2017","journal-title":"arXiv:1706.03762"},{"doi-asserted-by":"publisher","key":"ref3","DOI":"10.5244\/C.30.87"},{"doi-asserted-by":"publisher","key":"ref4","DOI":"10.1109\/CVPR.2014.81"},{"doi-asserted-by":"publisher","key":"ref5","DOI":"10.1007\/978-3-319-46484-8_29"},{"doi-asserted-by":"publisher","key":"ref6","DOI":"10.1109\/CVPR.2015.7298965"},{"doi-asserted-by":"publisher","key":"ref7","DOI":"10.1007\/978-1-4615-5529-2_5"},{"doi-asserted-by":"publisher","key":"ref8","DOI":"10.1109\/TFUZZ.2021.3122265"},{"doi-asserted-by":"publisher","key":"ref9","DOI":"10.1093\/bioinformatics\/btaa775"},{"doi-asserted-by":"publisher","key":"ref10","DOI":"10.1109\/TKDE.2019.2914200"},{"doi-asserted-by":"publisher","key":"ref11","DOI":"10.18653\/v1\/P19-1441"},{"doi-asserted-by":"publisher","key":"ref12","DOI":"10.18653\/v1\/P19-1595"},{"doi-asserted-by":"publisher","key":"ref13","DOI":"10.1007\/978-3-030-01246-5_25"},{"doi-asserted-by":"publisher","key":"ref14","DOI":"10.1109\/CVPR.2019.00195"},{"doi-asserted-by":"publisher","key":"ref15","DOI":"10.1007\/978-3-030-58565-5_41"},{"doi-asserted-by":"publisher","key":"ref16","DOI":"10.1109\/ICCV.2019.00663"},{"doi-asserted-by":"publisher","key":"ref17","DOI":"10.1109\/CVPR.2018.00904"},{"doi-asserted-by":"publisher","key":"ref18","DOI":"10.48550\/arXiv.1503.02531"},{"year":"2009","author":"Krizhevsky","article-title":"Learning multiple layers of features from tiny images","key":"ref19"},{"doi-asserted-by":"publisher","key":"ref20","DOI":"10.1109\/ICCV.2015.425"},{"doi-asserted-by":"publisher","key":"ref21","DOI":"10.1109\/CVPR.2016.433"},{"doi-asserted-by":"publisher","key":"ref22","DOI":"10.1109\/CVPR.2019.00332"},{"doi-asserted-by":"publisher","key":"ref23","DOI":"10.1109\/CVPR.2018.00077"},{"doi-asserted-by":"publisher","key":"ref24","DOI":"10.1109\/CVPR.2019.00197"},{"doi-asserted-by":"publisher","key":"ref25","DOI":"10.1109\/CVPR.2018.00810"},{"doi-asserted-by":"publisher","key":"ref26","DOI":"10.1109\/CVPR.2019.00282"},{"key":"ref27","first-page":"12060","article-title":"Pareto multi-task learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Lin"},{"key":"ref28","first-page":"525","article-title":"Multi-task learning as multi-objective optimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Sener"},{"doi-asserted-by":"publisher","key":"ref29","DOI":"10.1007\/978-3-030-01270-0_17"},{"key":"ref30","first-page":"5824","article-title":"Gradient surgery for multi-task learning","volume-title":"Advances in Neural Information Processing Systems","volume":"33","author":"Yu","year":"2020"},{"key":"ref31","article-title":"Do deep nets really need to be deep?","author":"Ba","year":"2013","journal-title":"arXiv:1312.6184"},{"key":"ref32","article-title":"FitNets: Hints for thin deep nets","author":"Romero","year":"2014","journal-title":"arXiv:1412.6550"},{"doi-asserted-by":"publisher","key":"ref33","DOI":"10.1007\/978-3-030-58520-4_28"},{"key":"ref34","first-page":"1","article-title":"Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer","volume-title":"Proc. ICLR","author":"Zagoruyko"},{"doi-asserted-by":"publisher","key":"ref35","DOI":"10.1609\/aaai.v30i1.10449"},{"doi-asserted-by":"publisher","key":"ref36","DOI":"10.1007\/978-3-030-58539-6_37"},{"doi-asserted-by":"publisher","key":"ref37","DOI":"10.18653\/v1\/2020.acl-main.195"},{"doi-asserted-by":"publisher","key":"ref38","DOI":"10.26615\/978-954-452-056-4_050"},{"doi-asserted-by":"publisher","key":"ref39","DOI":"10.1007\/978-3-030-65414-6_13"},{"doi-asserted-by":"publisher","key":"ref40","DOI":"10.1109\/CVPR42600.2020.01360"},{"doi-asserted-by":"publisher","key":"ref41","DOI":"10.1109\/CVPR42600.2020.00399"},{"doi-asserted-by":"publisher","key":"ref42","DOI":"10.1109\/ICCV.2015.314"},{"doi-asserted-by":"publisher","key":"ref43","DOI":"10.1109\/ICCV.2019.00512"},{"key":"ref44","first-page":"8728","article-title":"AdaShare: Learning what to share for efficient deep multi-task learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Sun"},{"key":"ref45","first-page":"8024","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Paszke","year":"2019"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9668973\/09740124.pdf?arnumber=9740124","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T00:33:44Z","timestamp":1705538024000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9740124\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":45,"URL":"https:\/\/doi.org\/10.1109\/access.2022.3161622","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2022]]}}}