{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T06:37:56Z","timestamp":1763534276321,"version":"3.37.3"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","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:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy through the Lawrence Livermore National Laboratory","doi-asserted-by":"publisher","award":["DE-AC52-07NA27344"],"award-info":[{"award-number":["DE-AC52-07NA27344"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Lawrence Livermore National Security, LLC"},{"DOI":"10.13039\/100000185","name":"Defense Advanced Research Projects Agency","doi-asserted-by":"publisher","award":["HR00112290073"],"award-info":[{"award-number":["HR00112290073"]}],"id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/access.2023.3239673","type":"journal-article","created":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T18:39:11Z","timestamp":1674671951000},"page":"12858-12869","source":"Crossref","is-referenced-by-count":4,"title":["The Surprising Effectiveness of Deep Orthogonal Procrustes Alignment in Unsupervised Domain Adaptation"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2183-8577","authenticated-orcid":false,"given":"Kowshik","family":"Thopalli","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Geometric Media Laboratory, Arizona State University, Tempe, AZ, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4186-3502","authenticated-orcid":false,"given":"Rushil","family":"Anirudh","sequence":"additional","affiliation":[{"name":"Lawrence Livermore National Laboratory, Livermore, CA, USA"}]},{"given":"Pavan","family":"Turaga","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Geometric Media Laboratory, Arizona State University, Tempe, AZ, USA"}]},{"given":"Jayaraman J.","family":"Thiagarajan","sequence":"additional","affiliation":[{"name":"Lawrence Livermore National Laboratory, Livermore, CA, USA"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.05.083"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.368"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126344"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6247911"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00887"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01225-0_28"},{"key":"ref9","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Long"},{"key":"ref10","first-page":"2208","article-title":"Deep transfer learning with joint adaptation networks","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Long"},{"key":"ref11","first-page":"1647","article-title":"Conditional adversarial domain adaptation","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Long"},{"key":"ref12","article-title":"VisDA: The visual domain adaptation challenge","volume-title":"arXiv:1710.06924","author":"Peng","year":"2017"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3054609"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3203736"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3176719"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2091281"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-58347-1_8"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.5244\/C.29.24"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"ref21","first-page":"1","article-title":"A DIRT-T approach to unsupervised domain adaptation","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Shu"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-58347-1_10"},{"key":"ref24","first-page":"1994","article-title":"CyCADA: Cycle-consistent adversarial domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Hoffman"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1007\/978-3-319-70139-4","article-title":"Unsupervised image-to-image translation networks","volume-title":"Advances in Neural Information Processing Systems","author":"Liu","year":"2017"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3147039"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3110179"},{"key":"ref29","first-page":"2988","article-title":"Asymmetric tri-training for unsupervised domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Saito"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00608"},{"key":"ref31","first-page":"6028","article-title":"Do we really need to access the source data? Source hypothesis transfer for unsupervised domain adaptation","volume-title":"Proc. 37th Int. Conf. Mach. Learn. (ICML)","author":"Liang"},{"key":"ref32","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Finn"},{"key":"ref33","first-page":"1","article-title":"Siamese neural networks for one-shot image recognition","volume-title":"Proc. ICML Deep Learn. Workshop","volume":"2","author":"Koch"},{"key":"ref34","first-page":"3630","article-title":"Matching networks for one shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Vinyals"},{"key":"ref35","first-page":"1842","article-title":"Meta-learning with memory-augmented neural networks","volume-title":"Proc. Int. Conf. Int. Conf. Mach. Learn. (ICML)","author":"Santoro"},{"key":"ref36","first-page":"2554","article-title":"Meta networks","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Munkhdalai"},{"key":"ref37","first-page":"1","article-title":"Optimization as a model for few-shot learning","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Ravi"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2017-1118"},{"key":"ref39","article-title":"Auxiliary tasks in multi-task learning","volume-title":"arXiv:1805.06334","author":"Liebel","year":"2018"},{"key":"ref40","first-page":"1","article-title":"Reinforcement learning with unsupervised auxiliary tasks","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Jaderberg"},{"key":"ref41","first-page":"1","article-title":"Self-supervised generalisation with meta auxiliary learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Liu"},{"key":"ref42","first-page":"1","article-title":"Self-ensembling for visual domain adaptation","volume-title":"Int. Conf. Learn. Represent. (ICLR)","author":"French"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.2307\/j.ctvcm4g18.8"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref46","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Kingma"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/34.291440"},{"key":"ref48","first-page":"18","article-title":"MNIST handwritten digit database","volume":"2","author":"LeCun","year":"2010","journal-title":"ATT Labs"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.2118\/18761-MS"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.572"},{"key":"ref51","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Krizhevsky"},{"key":"ref52","first-page":"1180","article-title":"Unsupervised domain adaptation by backpropagation","volume-title":"Proc. Int. Conf. Int. Conf. Mach. Learn. (ICML)","author":"Ganin"},{"key":"ref53","first-page":"136","article-title":"Unsupervised domain adaptation with residual transfer networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Long"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01237-3_9"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01228-1_10"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/6287639\/10005208\/10025714-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10005208\/10025714.pdf?arnumber=10025714","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T06:20:39Z","timestamp":1707805239000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10025714\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":55,"URL":"https:\/\/doi.org\/10.1109\/access.2023.3239673","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2023]]}}}