{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T22:52:01Z","timestamp":1729637521198,"version":"3.28.0"},"reference-count":31,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,23]]},"DOI":"10.23919\/eusipco54536.2021.9615974","type":"proceedings-article","created":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T21:55:53Z","timestamp":1639000553000},"page":"1371-1375","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Source Domain Adaptation with Sinkhorn Barycenter"],"prefix":"10.23919","author":[{"given":"Tatsuya","family":"Komatsu","sequence":"first","affiliation":[]},{"given":"Tomoko","family":"Matsui","sequence":"additional","affiliation":[]},{"given":"Junbin","family":"Gao","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref31","first-page":"1961","article-title":"Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration","author":"altschuler","year":"2017","journal-title":"Proc of NIPS"},{"journal-title":"Computational Optimal Transport With Applications to Data Science Foundations and Trends in Machine Learning Series","year":"2019","author":"peyre","key":"ref30"},{"key":"ref10","first-page":"1","article-title":"Central moment discrepancy (CMD) for domain-invariant representation learning","author":"zeilinger","year":"2017","journal-title":"Proc of ICLR"},{"key":"ref11","first-page":"2208","article-title":"Deep transfer learning with joint adaptation networks","author":"long","year":"2017","journal-title":"Proc of ICML"},{"key":"ref12","article-title":"Deep domain confusion: Maximizing for domain invariance","author":"tzeng","year":"2014","journal-title":"ArXiv"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-13560-1_76"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3240508.3240512"},{"key":"ref15","first-page":"4119","article-title":"Supervised representation learning: Transfer learning with deep autoencoders","author":"zhuang","year":"2015","journal-title":"ln Proc of IlCAI"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00417"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00149"},{"key":"ref19","first-page":"6798","article-title":"Extracting relationships by multidomain matching","author":"li","year":"2018","journal-title":"Proc of NIPS"},{"key":"ref28","first-page":"1","article-title":"Unsupervised domain adaptation by backpropagation","author":"ganin","year":"2015","journal-title":"Proc of ICML"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2012.43"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.161"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2665976"},{"key":"ref6","first-page":"469","article-title":"Coupled generative adversarial networks","author":"liu","year":"2016","journal-title":"Proc of NIPS"},{"key":"ref29","first-page":"2681","article-title":"Interpolating between optimal transport and mmd using sinkhorn divergences","author":"feydy","year":"2019","journal-title":"Proc of AISTATS"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2913096"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00219"},{"key":"ref7","first-page":"4","article-title":"Adversar-ial discriminative domain adaptation","volume":"1","author":"tzeng","year":"2017","journal-title":"Proc of CVP R"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2017.02.019"},{"key":"ref9","first-page":"1","article-title":"Return of frustratingly easy domain adaptation","author":"sun","year":"2016","journal-title":"Proc Of AAAI"},{"key":"ref1","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v31i1.10867","article-title":"Multi-view clustering via deep matrix factorization","author":"zhao","year":"2017","journal-title":"Proc Of AAAI"},{"key":"ref20","first-page":"685","article-title":"Fast computation of Wasserstein barycenters","author":"cuturi","year":"2014","journal-title":"Proc of ICML"},{"key":"ref22","article-title":"Sinkhorn barycenters with free support via Frank-Wolfe algorithm","author":"luise","year":"2020","journal-title":"Proc of NIPS"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1137\/141000439"},{"journal-title":"Machine Learning A Probabilistic Perspective","year":"2012","author":"murphy","key":"ref24"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1137\/100805741"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref25","first-page":"2292","article-title":"Sinkhorn distances: Lightspeed computation of optimal transport","author":"cuturi","year":"2013","journal-title":"Proc of NIPS"}],"event":{"name":"2021 29th European Signal Processing Conference (EUSIPCO)","start":{"date-parts":[[2021,8,23]]},"location":"Dublin, Ireland","end":{"date-parts":[[2021,8,27]]}},"container-title":["2021 29th European Signal Processing Conference (EUSIPCO)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9615915\/9615917\/09615974.pdf?arnumber=9615974","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T18:13:14Z","timestamp":1673979194000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9615974\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,23]]},"references-count":31,"URL":"https:\/\/doi.org\/10.23919\/eusipco54536.2021.9615974","relation":{},"subject":[],"published":{"date-parts":[[2021,8,23]]}}}