{"id":"https://openalex.org/W4306729030","doi":"https://doi.org/10.1109/tpami.2022.3215150","title":"Towards Accurate and Robust Domain Adaptation Under Multiple Noisy Environments","display_name":"Towards Accurate and Robust Domain Adaptation Under Multiple Noisy Environments","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4306729030","doi":"https://doi.org/10.1109/tpami.2022.3215150","pmid":"https://pubmed.ncbi.nlm.nih.gov/36251911"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2022.3215150","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2022.3215150","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5086112796","display_name":"Zhongyi Han","orcid":"https://orcid.org/0000-0003-2851-193X"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhongyi Han","raw_affiliation_strings":["School of Software, Shandong University, Jinan, China"],"affiliations":[{"raw_affiliation_string":"School of Software, Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088715258","display_name":"Xian-Jin Gui","orcid":null},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xian-Jin Gui","raw_affiliation_strings":["National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038081609","display_name":"Haoliang Sun","orcid":"https://orcid.org/0000-0001-7715-5682"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoliang Sun","raw_affiliation_strings":["School of Software, Shandong University, Jinan, China"],"affiliations":[{"raw_affiliation_string":"School of Software, Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100672590","display_name":"Yilong Yin","orcid":"https://orcid.org/0000-0002-8465-1294"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yilong Yin","raw_affiliation_strings":["School of Software, Shandong University, Jinan, China"],"affiliations":[{"raw_affiliation_string":"School of Software, Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100386630","display_name":"Shuo Li","orcid":"https://orcid.org/0000-0002-5184-3230"},"institutions":[{"id":"https://openalex.org/I58956616","display_name":"Case Western Reserve University","ror":"https://ror.org/051fd9666","country_code":"US","type":"education","lineage":["https://openalex.org/I58956616"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shuo Li","raw_affiliation_strings":["Department of Computer and Data Science, Case Western Reserve University, Cleveland, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer and Data Science, Case Western Reserve University, Cleveland, USA","institution_ids":["https://openalex.org/I58956616"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5086112796"],"corresponding_institution_ids":["https://openalex.org/I154099455"],"apc_list":null,"apc_paid":null,"fwci":4.6885,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.95456055,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"45","issue":"5","first_page":"1","last_page":"18"},"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":0.9998000264167786,"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":0.9998000264167786,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.982699990272522,"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":0.9789999723434448,"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":0.8329782485961914},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5838274955749512},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5815242528915405},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5769577622413635},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5616444945335388},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5207021832466125},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.48349741101264954},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.47001054883003235},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.469434529542923},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.44919371604919434},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.42112377285957336},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3763435184955597},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3604235351085663},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33390313386917114},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.19723191857337952},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1035689115524292}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8329782485961914},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5838274955749512},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5815242528915405},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5769577622413635},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5616444945335388},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5207021832466125},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.48349741101264954},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.47001054883003235},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.469434529542923},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.44919371604919434},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.42112377285957336},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3763435184955597},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3604235351085663},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33390313386917114},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.19723191857337952},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1035689115524292},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2022.3215150","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2022.3215150","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:36251911","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36251911","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on pattern analysis and machine intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.5299999713897705,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":82,"referenced_works":["https://openalex.org/W1722318740","https://openalex.org/W1805361780","https://openalex.org/W1997731668","https://openalex.org/W2010135967","https://openalex.org/W2019363670","https://openalex.org/W2034841618","https://openalex.org/W2062291443","https://openalex.org/W2104094955","https://openalex.org/W2108598243","https://openalex.org/W2112483442","https://openalex.org/W2115403315","https://openalex.org/W2128053425","https://openalex.org/W2131953535","https://openalex.org/W2156557681","https://openalex.org/W2167460663","https://openalex.org/W2194775991","https://openalex.org/W2540093921","https://openalex.org/W2566079294","https://openalex.org/W2593597837","https://openalex.org/W2593768305","https://openalex.org/W2627183927","https://openalex.org/W2743200750","https://openalex.org/W2798681837","https://openalex.org/W2904549000","https://openalex.org/W2945906052","https://openalex.org/W2962687275","https://openalex.org/W2963393201","https://openalex.org/W2964155802","https://openalex.org/W2964292098","https://openalex.org/W2970778145","https://openalex.org/W2978625989","https://openalex.org/W2979509742","https://openalex.org/W2990019157","https://openalex.org/W2996060033","https://openalex.org/W3026931681","https://openalex.org/W3035235554","https://openalex.org/W3045801508","https://openalex.org/W3094447715","https://openalex.org/W3118826713","https://openalex.org/W3194427711","https://openalex.org/W3202345803","https://openalex.org/W4205714457","https://openalex.org/W4282938511","https://openalex.org/W4283806925","https://openalex.org/W4288083766","https://openalex.org/W6636267554","https://openalex.org/W6639480849","https://openalex.org/W6676141320","https://openalex.org/W6677082149","https://openalex.org/W6679390333","https://openalex.org/W6680769272","https://openalex.org/W6681588610","https://openalex.org/W6682658890","https://openalex.org/W6682778277","https://openalex.org/W6683341053","https://openalex.org/W6683633756","https://openalex.org/W6695692224","https://openalex.org/W6713955831","https://openalex.org/W6730389898","https://openalex.org/W6734939518","https://openalex.org/W6735050833","https://openalex.org/W6737976933","https://openalex.org/W6738471490","https://openalex.org/W6739901393","https://openalex.org/W6742511895","https://openalex.org/W6745136726","https://openalex.org/W6750109254","https://openalex.org/W6750523955","https://openalex.org/W6751420435","https://openalex.org/W6751647823","https://openalex.org/W6751979845","https://openalex.org/W6753772092","https://openalex.org/W6755166560","https://openalex.org/W6761139768","https://openalex.org/W6761839128","https://openalex.org/W6762179710","https://openalex.org/W6771630921","https://openalex.org/W6777349897","https://openalex.org/W6780205002","https://openalex.org/W6784323503","https://openalex.org/W6789476682","https://openalex.org/W6801615203"],"related_works":["https://openalex.org/W3125011624","https://openalex.org/W1508631387","https://openalex.org/W2370917603","https://openalex.org/W2952760143","https://openalex.org/W4287880334","https://openalex.org/W3006513224","https://openalex.org/W4366700029","https://openalex.org/W2017776670","https://openalex.org/W4210897550","https://openalex.org/W3105973526"],"abstract_inverted_index":{"In":[0,47],"many":[1],"non-stationary":[2],"environments,":[3],"machine":[4],"learning":[5,89,140],"algorithms":[6],"usually":[7],"confront":[8],"the":[9,32,74,80,106,112,124,132,143,175,179,189],"distribution":[10,108],"shift":[11],"scenarios.":[12],"Previous":[13],"domain":[14,36,57],"adaptation":[15],"methods":[16],"have":[17,70],"achieved":[18],"great":[19],"success.":[20],"However,":[21],"they":[22],"would":[23],"lose":[24],"algorithm":[25,186],"robustness":[26],"in":[27,196],"multiple":[28],"noisy":[29,107],"environments":[30],"where":[31],"examples":[33,129],"of":[34,82,114,127,171],"source":[35,83,94,115],"become":[37],"corrupted":[38],"by":[39],"label":[40],"noise,":[41,43],"feature":[42,150],"or":[44],"open-set":[45],"noise.":[46],"this":[48],"paper,":[49],"we":[50,85,153],"report":[51],"our":[52,185],"attempt":[53],"toward":[54],"achieving":[55],"noise-robust":[56],"adaptation.":[58],"We":[59,96,117,135],"first":[60],"give":[61],"a":[62,91,98],"theoretical":[63],"analysis":[64],"and":[65,147,178],"find":[66],"that":[67,162,184],"different":[68],"noises":[69],"disparate":[71],"impacts":[72],"on":[73,174],"expected":[75],"target":[76],"risk.":[77,95],"To":[78],"eliminate":[79],"effect":[81,145],"noises,":[84],"propose":[86,118],"offline":[87],"curriculum":[88],"minimizing":[90],"newly-defined":[92],"empirical":[93,172],"suggest":[97,137],"proxy":[99],"distribution-based":[100],"margin":[101],"discrepancy":[102],"to":[103,110,130,141],"gradually":[104],"decrease":[105],"distance":[109],"reduce":[111],"impact":[113],"noises.":[116],"an":[119,159],"energy":[120],"estimator":[121],"for":[122,167],"assessing":[123],"outlier":[125],"degree":[126],"open-set-noise":[128],"defeat":[131],"harmful":[133],"influence.":[134],"also":[136],"robust":[138],"parameter":[139],"mitigate":[142],"negative":[144],"further":[146],"learn":[148],"domain-invariant":[149],"representations.":[151],"Finally,":[152],"seamlessly":[154],"transform":[155],"these":[156],"components":[157],"into":[158],"adversarial":[160],"network":[161],"performs":[163],"efficient":[164],"joint":[165],"optimization":[166],"them.":[168],"A":[169],"series":[170],"studies":[173],"benchmark":[176],"datasets":[177],"COVID-19":[180],"screening":[181],"task":[182],"show":[183],"remarkably":[187],"outperforms":[188],"state-of-the-art,":[190],"with":[191],"over":[192],"10%":[193],"accuracy":[194],"improvements":[195],"some":[197],"transfer":[198],"tasks.":[199]},"counts_by_year":[{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":8}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
