{"id":"https://openalex.org/W4283258238","doi":"https://doi.org/10.1145/3529836.3529858","title":"Self-supervised Domain Adaptation Model Based on Contrastive Learning","display_name":"Self-supervised Domain Adaptation Model Based on Contrastive Learning","publication_year":2022,"publication_date":"2022-02-18","ids":{"openalex":"https://openalex.org/W4283258238","doi":"https://doi.org/10.1145/3529836.3529858"},"language":"en","primary_location":{"id":"doi:10.1145/3529836.3529858","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3529836.3529858","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","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/A5101149641","display_name":"Ya Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ya Ma","raw_affiliation_strings":["College of Cyber Science, Nankai University, College of Cyber Science, Nankai University, China"],"affiliations":[{"raw_affiliation_string":"College of Cyber Science, Nankai University, College of Cyber Science, Nankai University, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100458392","display_name":"Biao Chen","orcid":"https://orcid.org/0000-0002-3559-2515"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Biao Chen","raw_affiliation_strings":["College of Cyber Science, Nankai University, College of Cyber Science, Nankai University, China"],"affiliations":[{"raw_affiliation_string":"College of Cyber Science, Nankai University, College of Cyber Science, Nankai University, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100424993","display_name":"Ziwei Li","orcid":"https://orcid.org/0000-0002-1999-0011"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziwei Li","raw_affiliation_strings":["College of Cyber Science, Nankai University, College of Cyber Science, Nankai University, China"],"affiliations":[{"raw_affiliation_string":"College of Cyber Science, Nankai University, College of Cyber Science, Nankai University, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084393924","display_name":"Gang Bai","orcid":"https://orcid.org/0000-0001-9161-2173"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang Bai","raw_affiliation_strings":["College of Cyber Science, Nankai University, College of Cyber Science, Nankai University, China"],"affiliations":[{"raw_affiliation_string":"College of Cyber Science, Nankai University, College of Cyber Science, Nankai University, China","institution_ids":["https://openalex.org/I205237279"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101149641"],"corresponding_institution_ids":["https://openalex.org/I205237279"],"apc_list":null,"apc_paid":null,"fwci":0.2652,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.59857463,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"44","last_page":"50"},"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":0.9986000061035156,"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":0.9986000061035156,"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/computer-science","display_name":"Computer science","score":0.7740652561187744},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.731420636177063},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7063020467758179},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5895779132843018},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5689690113067627},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.5633176565170288},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.508508563041687},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47687917947769165},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4164065718650818},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4124307632446289},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3825607895851135},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3286607265472412},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.16858059167861938},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13157916069030762}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7740652561187744},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.731420636177063},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7063020467758179},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5895779132843018},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5689690113067627},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.5633176565170288},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.508508563041687},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47687917947769165},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4164065718650818},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4124307632446289},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3825607895851135},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3286607265472412},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.16858059167861938},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13157916069030762},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3529836.3529858","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3529836.3529858","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7799999713897705,"display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G5638166543","display_name":null,"funder_award_id":"No. 18ZXZNGX00200","funder_id":"https://openalex.org/F4320323993","funder_display_name":"Natural Science Foundation of Tianjin City"}],"funders":[{"id":"https://openalex.org/F4320323993","display_name":"Natural Science Foundation of Tianjin City","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1510014385","https://openalex.org/W1722318740","https://openalex.org/W1882958252","https://openalex.org/W2108598243","https://openalex.org/W2151058089","https://openalex.org/W2194775991","https://openalex.org/W2478454054","https://openalex.org/W2597507805","https://openalex.org/W2627183927","https://openalex.org/W2963532621","https://openalex.org/W3034738317","https://openalex.org/W3035519852","https://openalex.org/W3177525997","https://openalex.org/W4297792979","https://openalex.org/W6698183232","https://openalex.org/W6739944922","https://openalex.org/W6745831770","https://openalex.org/W6746171285","https://openalex.org/W6750109254"],"related_works":["https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W3119773509","https://openalex.org/W3208297503","https://openalex.org/W2889153461","https://openalex.org/W2964117661","https://openalex.org/W4388405611","https://openalex.org/W2619127353","https://openalex.org/W2147093486"],"abstract_inverted_index":{"Contrastive":[0],"learning":[1,7,47,87],"is":[2],"a":[3,32],"typical":[4],"discriminative":[5],"self-supervised":[6,33],"method,":[8],"which":[9,41],"can":[10],"learn":[11,118],"knowledge":[12],"from":[13],"unlabeled":[14,23],"data.":[15,26],"Unsupervised":[16],"domain":[17,25,34,68,75],"adaptation":[18,35],"(UDA)":[19],"aims":[20],"to":[21,48,61,77,88,117],"predict":[22],"target":[24,67],"In":[27,52],"this":[28,53],"paper,":[29],"we":[30,55],"propose":[31],"model":[36,116,128],"based":[37],"on":[38,129],"contrastive":[39,46,86],"learning,":[40],"applies":[42],"the":[43,58,63,66,72,79,90,95,100,104,115,123],"idea":[44],"of":[45,65,97,99,125],"UDA,":[49],"named":[50],"siam-DAN.":[51],"model,":[54,91],"first":[56],"use":[57],"clustering":[59],"method":[60],"obtain":[62],"pseudo-labels":[64],"data,":[69],"then":[70],"combine":[71],"labeled":[73],"source":[74],"data":[76],"construct":[78],"positive":[80],"and":[81,112,135,137],"negative":[82],"examples":[83],"required":[84],"for":[85],"train":[89],"so":[92],"that":[93],"makes":[94],"distribution":[96],"samples":[98],"same":[101],"class":[102],"in":[103],"representation":[105],"space":[106],"overlap":[107],"as":[108,110],"much":[109],"possible":[111],"finally":[113],"enable":[114],"domain-invariant":[119],"features.":[120],"We":[121],"evaluate":[122],"performance":[124],"our":[126],"proposed":[127],"three":[130],"public":[131],"benchmarks:":[132],"Office-31,":[133],"Office-Home,":[134],"VisDA-2017,":[136],"achieve":[138],"relatively":[139],"competitive":[140],"results.":[141]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
