{"id":"https://openalex.org/W4205604796","doi":"https://doi.org/10.23919/eusipco54536.2021.9615974","title":"Multi-Source Domain Adaptation with Sinkhorn Barycenter","display_name":"Multi-Source Domain Adaptation with Sinkhorn Barycenter","publication_year":2021,"publication_date":"2021-08-23","ids":{"openalex":"https://openalex.org/W4205604796","doi":"https://doi.org/10.23919/eusipco54536.2021.9615974"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco54536.2021.9615974","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco54536.2021.9615974","pdf_url":null,"source":{"id":"https://openalex.org/S4363607854","display_name":"2021 29th European Signal Processing Conference (EUSIPCO)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 29th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113730169","display_name":"Tatsuya Komatsu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210096607","display_name":"Line Corporation (Japan)","ror":"https://ror.org/00qg8pm87","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210096607","https://openalex.org/I60922564"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Tatsuya Komatsu","raw_affiliation_strings":["LINE Corporation, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"LINE Corporation, Tokyo, Japan","institution_ids":["https://openalex.org/I4210096607"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067714865","display_name":"Tomoko Matsui","orcid":"https://orcid.org/0000-0003-3201-6106"},"institutions":[{"id":"https://openalex.org/I4210134673","display_name":"The Institute of Statistical Mathematics","ror":"https://ror.org/03jcejr58","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1319490839","https://openalex.org/I4210134673","https://openalex.org/I4210158934"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tomoko Matsui","raw_affiliation_strings":["The Institute of Statistical Mathematics, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The Institute of Statistical Mathematics, Tokyo, Japan","institution_ids":["https://openalex.org/I4210134673"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015817857","display_name":"Junbin Gao","orcid":"https://orcid.org/0000-0001-9803-0256"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Junbin Gao","raw_affiliation_strings":["The University of Sydney, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"The University of Sydney, Sydney, Australia","institution_ids":["https://openalex.org/I129604602"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5113730169"],"corresponding_institution_ids":["https://openalex.org/I4210096607"],"apc_list":null,"apc_paid":null,"fwci":0.1257,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.39100766,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":"1","issue":null,"first_page":"1371","last_page":"1375"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9758999943733215,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10515","display_name":"Cancer-related molecular mechanisms research","score":0.9629999995231628,"subfield":{"id":"https://openalex.org/subfields/1306","display_name":"Cancer Research"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7420954704284668},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6561387777328491},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5706449151039124},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5187295079231262},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5024945735931396},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.478372722864151},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.46874454617500305},{"id":"https://openalex.org/keywords/extractor","display_name":"Extractor","score":0.45876544713974},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4520551562309265},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3569202423095703},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17280811071395874}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7420954704284668},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6561387777328491},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5706449151039124},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5187295079231262},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5024945735931396},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.478372722864151},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.46874454617500305},{"id":"https://openalex.org/C117978034","wikidata":"https://www.wikidata.org/wiki/Q5422192","display_name":"Extractor","level":2,"score":0.45876544713974},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4520551562309265},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3569202423095703},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17280811071395874},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/eusipco54536.2021.9615974","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco54536.2021.9615974","pdf_url":null,"source":{"id":"https://openalex.org/S4363607854","display_name":"2021 29th European Signal Processing Conference (EUSIPCO)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 29th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1503398984","https://openalex.org/W1565327149","https://openalex.org/W1629559917","https://openalex.org/W1882958252","https://openalex.org/W1968333723","https://openalex.org/W2036996178","https://openalex.org/W2104094955","https://openalex.org/W2110158442","https://openalex.org/W2112796928","https://openalex.org/W2158131535","https://openalex.org/W2169529055","https://openalex.org/W2261310161","https://openalex.org/W2586686153","https://openalex.org/W2592141621","https://openalex.org/W2593539611","https://openalex.org/W2593768305","https://openalex.org/W2605146283","https://openalex.org/W2620318602","https://openalex.org/W2884771968","https://openalex.org/W2890487450","https://openalex.org/W2943996654","https://openalex.org/W2949573729","https://openalex.org/W2963168418","https://openalex.org/W2963217615","https://openalex.org/W2963275094","https://openalex.org/W2963506806","https://openalex.org/W2963784072","https://openalex.org/W2964266860","https://openalex.org/W2964278684","https://openalex.org/W2981720610","https://openalex.org/W4288345978","https://openalex.org/W6633949838","https://openalex.org/W6636708570","https://openalex.org/W6639480849","https://openalex.org/W6682962330","https://openalex.org/W6685415078","https://openalex.org/W6692729736","https://openalex.org/W6713955831","https://openalex.org/W6720691552","https://openalex.org/W6734871034","https://openalex.org/W6738958611","https://openalex.org/W6754838503","https://openalex.org/W6756138707","https://openalex.org/W6762415961"],"related_works":["https://openalex.org/W3082848404","https://openalex.org/W1979583797","https://openalex.org/W2016864125","https://openalex.org/W2372254676","https://openalex.org/W2080135837","https://openalex.org/W2793679056","https://openalex.org/W2945706271","https://openalex.org/W4387435415","https://openalex.org/W2114169842","https://openalex.org/W2535808783"],"abstract_inverted_index":{"We":[0],"describe":[1],"a":[2,24,101],"multi-source":[3,18],"and":[4,20,42,52,72,75,114],"unsupervised":[5],"domain":[6],"adaptation":[7],"method":[8,146],"using":[9,129],"Sinkhorn":[10,31,67,109,123],"barycenters,":[11],"which,":[12],"given":[13],"the":[14,28,40,49,58,63,66,70,76,79,83,92,108,116,122,130],"labeled":[15],"data":[16,22,37],"in":[17,23,39,100],"domains":[19,74,113],"unlabeled":[21],"target":[25,43,73],"domain,":[26],"uses":[27],"optimal":[29],"transport":[30],"distance":[32,68,110],"to":[33,121],"measure":[34],"gaps":[35],"between":[36],"distributions":[38],"source":[41,71,84],"domains.":[44,85],"For":[45],"end-to-end":[46],"classification":[47,80,128],"learning,":[48],"feature":[50,103],"extractor":[51],"classifier":[53],"are":[54],"simultaneously":[55],"estimated":[56],"on":[57,91,126],"basis":[59],"of":[60,65,78,136],"two":[61],"criteria:":[62],"minimization":[64,77],"for":[69,82],"loss":[81],"The":[86],"first":[87],"criterion":[88],"is":[89,134],"based":[90],"assumptions":[93],"that":[94,115,144],"domain-invariant":[95],"features":[96],"would":[97,118],"be":[98,119],"captured":[99],"latent":[102],"space":[104,117],"obtained":[105],"by":[106],"minimizing":[107],"among":[111],"all":[112],"close":[120],"barycenter.":[124],"Experiments":[125],"image":[127],"Digit-Five":[131],"dataset,":[132],"which":[133],"comprised":[135],"digit":[137],"datasets":[138],"from":[139],"five":[140],"different":[141],"domains,":[142],"demonstrated":[143],"our":[145],"outperforms":[147],"other":[148],"state-of-the-art":[149],"methods.":[150]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}