{"id":"https://openalex.org/W3039080100","doi":null,"title":"Sequential Unsupervised Domain Adaptation through Prototypical Distributions.","display_name":"Sequential Unsupervised Domain Adaptation through Prototypical Distributions.","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3039080100","mag":"3039080100"},"language":"en","primary_location":{"id":"mag:3039080100","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2007.00197","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2007.00197","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5040786186","display_name":"Mohammad Ghomi Rostami","orcid":"https://orcid.org/0000-0001-7873-1004","observed_orcids":["https://orcid.org/0000-0001-7873-1004"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohammad Rostami","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-7873-1004","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5101715504","display_name":"Aram Galstyan","orcid":"https://orcid.org/0000-0003-4215-0886","observed_orcids":["https://orcid.org/0000-0003-4215-0886"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aram Galstyan","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-4215-0886","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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":1.0,"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":1.0,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.0,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/unsupervised-domain-adaptation","display_name":"unsupervised domain adaptation","score":1.0},{"id":"https://openalex.org/keywords/sequential-learning","display_name":"sequential learning","score":0.9269999861717224},{"id":"https://openalex.org/keywords/discriminative-embedding","display_name":"discriminative embedding","score":0.7350000143051147},{"id":"https://openalex.org/keywords/knowledge-transfer","display_name":"knowledge transfer","score":0.8939999938011169},{"id":"https://openalex.org/keywords/classifier-adaptation","display_name":"classifier adaptation","score":0.5979999899864197}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7461650371551514},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7240945100784302},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7082377672195435},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.6628215909004211},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6203179955482483},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6161629557609558},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.6090071797370911},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.581626832485199},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5738943815231323},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5036024451255798},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.47209781408309937},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.4347638487815857},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4260522127151489},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3290332853794098},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32623493671417236},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1391160786151886},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0852007269859314},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3039080100","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2007.00197","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:3039080100","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2007.00197","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W22861983","https://openalex.org/W786520459","https://openalex.org/W1594039573","https://openalex.org/W1731081199","https://openalex.org/W1882958252","https://openalex.org/W2120354757","https://openalex.org/W2159291411","https://openalex.org/W2194775991","https://openalex.org/W2408201877","https://openalex.org/W2527330869","https://openalex.org/W2531958295","https://openalex.org/W2554314924","https://openalex.org/W2593768305","https://openalex.org/W2605488490","https://openalex.org/W2617573776","https://openalex.org/W2767657961","https://openalex.org/W2770120573","https://openalex.org/W2788768841","https://openalex.org/W2795155917","https://openalex.org/W2889326414","https://openalex.org/W2894126076","https://openalex.org/W2921342716","https://openalex.org/W2963532621","https://openalex.org/W2964117661","https://openalex.org/W2964288524","https://openalex.org/W2978573218","https://openalex.org/W2979920800","https://openalex.org/W2981466833","https://openalex.org/W2993682913","https://openalex.org/W2997117931"],"related_works":["https://openalex.org/W3087056222","https://openalex.org/W7171739613","https://openalex.org/W3145312446","https://openalex.org/W7126219550","https://openalex.org/W2948462823","https://openalex.org/W3198125731","https://openalex.org/W4403930465","https://openalex.org/W3173573777","https://openalex.org/W4221142729","https://openalex.org/W3126679255"],"abstract_inverted_index":{"We":[0,61,99,152,175],"develop":[1],"an":[2,18,131,165],"algorithm":[3,159],"for":[4,145,156,167],"unsupervised":[5],"domain":[6,16,21,54,58,77,96,113],"adaptation":[7],"(UDA)":[8],"of":[9,37,107,123,188],"a":[10,13,23,42,63,115,126],"classifier":[11],"from":[12,135],"labeled":[14,75,138],"source":[15,53,76,88,95,109,137],"to":[17,82,85,93,148,193],"unlabeled":[19,87],"target":[20,57,112,150,173],"in":[22,114,125,171],"sequential":[24,127],"learning":[25,44,124,196],"setting.":[26],"UDA":[27,197],"has":[28,70],"been":[29,71],"studied":[30],"extensively":[31],"recently":[32],"but":[33],"the":[34,38,47,52,56,68,74,86,94,105,108,111,121,136,149,157,168,172,186,189],"vast":[35],"majority":[36],"existing":[39,194],"methods":[40],"consider":[41,62],"joint":[43,195],"setting":[45],"where":[46,67],"model":[48,69],"is":[49],"trained":[50,72],"on":[51,73],"and":[55,79,110,140,184],"data":[59,78,139],"simultaneously.":[60],"more":[64],"practical":[65],"setting,":[66,128],"then":[80,141],"needs":[81],"be":[83],"adapted":[84],"domain,":[89],"without":[90],"having":[91],"access":[92],"training":[97],"data.":[98],"tackle":[100],"this":[101,143],"problem":[102],"by":[103,160],"aligning":[104],"distributions":[106],"discriminative":[116],"embedding":[117],"space.":[118],"To":[119],"overcome":[120],"challenges":[122],"we":[129],"learn":[130],"intermediate":[132],"prototypical":[133],"distribution":[134,144],"use":[142],"knowledge":[146],"transfer":[147],"domain.":[151,174],"provide":[153],"theoretical":[154],"justification":[155],"proposed":[158,190],"showing":[161],"that":[162],"it":[163],"optimizes":[164],"upper-bound":[166],"expected":[169],"risk":[170],"also":[176],"conduct":[177],"extensive":[178],"experiments":[179],"with":[180],"several":[181],"standard":[182],"benchmarks":[183],"demonstrate":[185],"competitiveness":[187],"method":[191],"compared":[192],"algorithms.":[198]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":5}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2020-07-10T00:00:00"}