{"id":"https://openalex.org/W4386598283","doi":"https://doi.org/10.1109/icip49359.2023.10222054","title":"Progressive Mixup Augmented Teacher-Student Learning for Unsupervised Domain Adaptation","display_name":"Progressive Mixup Augmented Teacher-Student Learning for Unsupervised Domain Adaptation","publication_year":2023,"publication_date":"2023-09-11","ids":{"openalex":"https://openalex.org/W4386598283","doi":"https://doi.org/10.1109/icip49359.2023.10222054"},"language":"en","primary_location":{"id":"doi:10.1109/icip49359.2023.10222054","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip49359.2023.10222054","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","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/A5063336907","display_name":"Aotian Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aotian Zheng","raw_affiliation_strings":["University of Washington,Seattle,WA,USA","University of Washington, Seattle, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Washington,Seattle,WA,USA","institution_ids":["https://openalex.org/I201448701"]},{"raw_affiliation_string":"University of Washington, Seattle, WA, USA","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103188406","display_name":"Jie Mei","orcid":"https://orcid.org/0000-0002-8421-7981"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jie Mei","raw_affiliation_strings":["University of Washington,Seattle,WA,USA","University of Washington, Seattle, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Washington,Seattle,WA,USA","institution_ids":["https://openalex.org/I201448701"]},{"raw_affiliation_string":"University of Washington, Seattle, WA, USA","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017289206","display_name":"Farron Wallace","orcid":"https://orcid.org/0000-0002-3690-9588"},"institutions":[{"id":"https://openalex.org/I1308126019","display_name":"National Oceanic and Atmospheric Administration","ror":"https://ror.org/02z5nhe81","country_code":"US","type":"government","lineage":["https://openalex.org/I1308126019","https://openalex.org/I1343035065"]},{"id":"https://openalex.org/I4210131645","display_name":"NOAA National Marine Fisheries Service Southeast Fisheries Science Center","ror":"https://ror.org/0396y0w87","country_code":"US","type":"government","lineage":["https://openalex.org/I106745318","https://openalex.org/I1308126019","https://openalex.org/I1343035065","https://openalex.org/I4210131645"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Farron Wallace","raw_affiliation_strings":["National Oceanic and Atmospheric Administration (NOAA),Southeast Fisheries Science Center,USA","Southeast Fisheries Science Center, National Oceanic and Atmospheric Administration (NOAA), USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Oceanic and Atmospheric Administration (NOAA),Southeast Fisheries Science Center,USA","institution_ids":["https://openalex.org/I1308126019","https://openalex.org/I4210131645"]},{"raw_affiliation_string":"Southeast Fisheries Science Center, National Oceanic and Atmospheric Administration (NOAA), USA","institution_ids":["https://openalex.org/I4210131645","https://openalex.org/I1308126019"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076561281","display_name":"Craig S. Rose","orcid":"https://orcid.org/0000-0002-1971-5722"},"institutions":[{"id":"https://openalex.org/I1308126019","display_name":"National Oceanic and Atmospheric Administration","ror":"https://ror.org/02z5nhe81","country_code":"US","type":"government","lineage":["https://openalex.org/I1308126019","https://openalex.org/I1343035065"]},{"id":"https://openalex.org/I4210109053","display_name":"NOAA National Marine Fisheries Service Alaska Fisheries Science Center","ror":"https://ror.org/01h7fye62","country_code":"US","type":"government","lineage":["https://openalex.org/I106745318","https://openalex.org/I1308126019","https://openalex.org/I1343035065","https://openalex.org/I4210109053"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Craig Rose","raw_affiliation_strings":["National Oceanic and Atmospheric Administration (NOAA),Alaska Fisheries Science Center,USA","Alaska Fisheries Science Center, National Oceanic and Atmospheric Administration (NOAA), USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Oceanic and Atmospheric Administration (NOAA),Alaska Fisheries Science Center,USA","institution_ids":["https://openalex.org/I1308126019"]},{"raw_affiliation_string":"Alaska Fisheries Science Center, National Oceanic and Atmospheric Administration (NOAA), USA","institution_ids":["https://openalex.org/I1308126019","https://openalex.org/I4210109053"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043238152","display_name":"Rania Hussein","orcid":"https://orcid.org/0000-0002-2859-9401"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rania Hussein","raw_affiliation_strings":["University of Washington,Seattle,WA,USA","University of Washington, Seattle, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Washington,Seattle,WA,USA","institution_ids":["https://openalex.org/I201448701"]},{"raw_affiliation_string":"University of Washington, Seattle, WA, USA","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101702810","display_name":"Jenq\u2013Neng Hwang","orcid":"https://orcid.org/0000-0002-8877-2421"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jenq-Neng Hwang","raw_affiliation_strings":["University of Washington,Seattle,WA,USA","University of Washington, Seattle, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Washington,Seattle,WA,USA","institution_ids":["https://openalex.org/I201448701"]},{"raw_affiliation_string":"University of Washington, Seattle, WA, USA","institution_ids":["https://openalex.org/I201448701"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6526,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.75253959,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"3030","last_page":"3034"},"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":0.9995999932289124,"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":0.9995999932289124,"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":0.9668999910354614,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.785271942615509},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7261825799942017},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.6541809439659119},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.6061975359916687},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6042497754096985},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6019994020462036},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.564212441444397},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.5383888483047485},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47699955105781555},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.4724605083465576},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44810807704925537},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4404746890068054},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4278859794139862},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3444363474845886},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0668572187423706}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.785271942615509},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7261825799942017},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.6541809439659119},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.6061975359916687},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6042497754096985},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6019994020462036},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.564212441444397},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.5383888483047485},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47699955105781555},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.4724605083465576},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44810807704925537},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4404746890068054},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4278859794139862},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3444363474845886},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0668572187423706},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip49359.2023.10222054","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip49359.2023.10222054","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.8100000023841858,"display_name":"Quality Education"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320338437","display_name":"Southeast Fisheries Science Center","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2593768305","https://openalex.org/W2627183927","https://openalex.org/W2766897166","https://openalex.org/W2788768841","https://openalex.org/W2795155917","https://openalex.org/W2883725317","https://openalex.org/W2953070460","https://openalex.org/W2963532621","https://openalex.org/W3014285644","https://openalex.org/W3021632667","https://openalex.org/W3025988480","https://openalex.org/W3034738317","https://openalex.org/W3095697905","https://openalex.org/W3176602994","https://openalex.org/W3201612248","https://openalex.org/W4307823382","https://openalex.org/W6639480849","https://openalex.org/W6683633756","https://openalex.org/W6713955831","https://openalex.org/W6733814495","https://openalex.org/W6746171285","https://openalex.org/W6750109254","https://openalex.org/W6775719109","https://openalex.org/W6779157540","https://openalex.org/W6788135285","https://openalex.org/W6801682309"],"related_works":["https://openalex.org/W3095487414","https://openalex.org/W2901026139","https://openalex.org/W3035557009","https://openalex.org/W3204418343","https://openalex.org/W2341113105","https://openalex.org/W2955172689","https://openalex.org/W3132602785","https://openalex.org/W3046182208","https://openalex.org/W2343346879","https://openalex.org/W2186589590"],"abstract_inverted_index":{"Unsupervised":[0],"Domain":[1],"Adaptation":[2],"(UDA)":[3],"aims":[4],"to":[5,14,38,81,120,129],"transfer":[6,122],"knowledge":[7,125],"learned":[8],"from":[9,57,126],"a":[10,22,95],"labeled":[11],"source":[12,113,128],"domain":[13,23,36,124,132],"an":[15,69],"unlabeled":[16],"target":[17,62,85,131,140],"domain,":[18],"mostly":[19],"through":[20],"learning":[21,74],"invariant":[24],"feature":[25],"representation.":[26],"Currently,":[27],"the":[28,58,76,88,112,118,127,130,135],"best":[29],"performing":[30],"UDA":[31,54,70,156],"methods":[32,55],"use":[33,94],"category":[34,52],"level":[35,53],"alignment":[37],"capture":[39],"fine-grained":[40,123],"information,":[41],"resulting":[42],"in":[43],"significantly":[44],"improved":[45],"performance":[46,152],"over":[47],"global":[48],"alignment.":[49],"While":[50],"successful,":[51],"suffer":[56],"unreliable":[59],"pseudo-labels":[60,86],"for":[61,87],"data.":[63],"In":[64],"this":[65],"paper,":[66],"we":[67,93],"propose":[68],"approach":[71],"with":[72],"teacher-student":[73,146],"where":[75],"teacher":[77],"network":[78],"is":[79],"used":[80],"provide":[82],"more":[83],"reliable":[84],"student":[89],"during":[90],"training.":[91],"Furthermore,":[92],"progressive":[96,143],"mixup":[97,144],"augmentation":[98],"strategy":[99,149],"which":[100],"generates":[101],"intermediate":[102,115],"samples":[103],"that":[104],"become":[105],"increasingly":[106],"target-dominant":[107],"as":[108],"training":[109,148],"progresses.":[110],"Aligning":[111],"and":[114,160],"domains":[116],"allows":[117],"model":[119],"gradually":[121],"while":[133],"minimizing":[134],"negative":[136],"impact":[137],"of":[138],"noisy":[139],"pseudo-labels.":[141],"This":[142],"augmented":[145],"(PMATS)":[147],"achieves":[150],"state-of-the-art":[151],"on":[153],"two":[154],"public":[155],"benchmark":[157],"datasets:":[158],"Office-31":[159],"Office-Home.":[161]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}