{"id":"https://openalex.org/W4378501083","doi":"https://doi.org/10.1145/3580305.3599481","title":"Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalization","display_name":"Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalization","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4378501083","doi":"https://doi.org/10.1145/3580305.3599481"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599481","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599481","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599481","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599481","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119011757","display_name":"Yunze Tong","orcid":"https://orcid.org/0009-0005-0305-0059"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunze Tong","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0009-0005-0305-0059","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049344673","display_name":"Junkun Yuan","orcid":"https://orcid.org/0000-0003-0012-7397"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junkun Yuan","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-0012-7397","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102796488","display_name":"Min Zhang","orcid":"https://orcid.org/0009-0001-1289-1571"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Min Zhang","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0009-0001-1289-1571","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038022012","display_name":"Didi Zhu","orcid":"https://orcid.org/0009-0004-6892-5357"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Didi Zhu","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0009-0004-6892-5357","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023446235","display_name":"Keli Zhang","orcid":"https://orcid.org/0000-0002-7883-0552"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Keli Zhang","raw_affiliation_strings":["Noah's Ark Lab, Huawei Technologies, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-7883-0552","affiliations":[{"raw_affiliation_string":"Noah's Ark Lab, Huawei Technologies, Shenzhen, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004882141","display_name":"Fei Wu","orcid":"https://orcid.org/0000-0003-2139-8807"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Wu","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-2139-8807","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041727387","display_name":"Kun Kuang","orcid":"https://orcid.org/0000-0001-7024-9790"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kun Kuang","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-7024-9790","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.5245,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.84486824,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2189","last_page":"2200"},"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":0.9853000044822693,"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":0.9599000215530396,"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/generalization","display_name":"Generalization","score":0.7656874656677246},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7016400098800659},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6092892289161682},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.6027230620384216},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5772445201873779},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.4666338562965393},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4581533670425415},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42510661482810974},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.415658175945282},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.344616174697876},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3203720152378082},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2073023021221161}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.7656874656677246},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7016400098800659},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6092892289161682},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.6027230620384216},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5772445201873779},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.4666338562965393},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4581533670425415},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42510661482810974},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.415658175945282},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.344616174697876},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3203720152378082},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2073023021221161},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3580305.3599481","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599481","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599481","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2305.15889","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.15889","pdf_url":"https://arxiv.org/pdf/2305.15889","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,"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":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3580305.3599481","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599481","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599481","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2015651080","display_name":null,"funder_award_id":"226-2022-00142, 226-2022-00051","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G3235401080","display_name":null,"funder_award_id":"62037001","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3405551049","display_name":null,"funder_award_id":"226-2022-00051","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4385699040","display_name":null,"funder_award_id":"U20A20387","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G590615799","display_name":null,"funder_award_id":"2021QNRC001","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G6665774762","display_name":null,"funder_award_id":"2021QNRC001","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G686355997","display_name":null,"funder_award_id":"226-2022-00142","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G7235758038","display_name":null,"funder_award_id":"226-2022-00051","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G8556648500","display_name":null,"funder_award_id":"62006207","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322927","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4378501083.pdf","grobid_xml":"https://content.openalex.org/works/W4378501083.grobid-xml"},"referenced_works_count":46,"referenced_works":["https://openalex.org/W2103868202","https://openalex.org/W2117539524","https://openalex.org/W2149298154","https://openalex.org/W2167366427","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2627183927","https://openalex.org/W2763549966","https://openalex.org/W2768783441","https://openalex.org/W2798658180","https://openalex.org/W2883386984","https://openalex.org/W2899575547","https://openalex.org/W2963043696","https://openalex.org/W2964124968","https://openalex.org/W2964288524","https://openalex.org/W2998115938","https://openalex.org/W3016970897","https://openalex.org/W3035524453","https://openalex.org/W3038022836","https://openalex.org/W3038585207","https://openalex.org/W3103934428","https://openalex.org/W3113148327","https://openalex.org/W3154419237","https://openalex.org/W3166235106","https://openalex.org/W3175215201","https://openalex.org/W3183864931","https://openalex.org/W3209356110","https://openalex.org/W3211549568","https://openalex.org/W4213244466","https://openalex.org/W4221168018","https://openalex.org/W4290875118","https://openalex.org/W4304080724","https://openalex.org/W4304084069","https://openalex.org/W4308748980","https://openalex.org/W4310079729","https://openalex.org/W4312106989","https://openalex.org/W4312232143","https://openalex.org/W4312376326","https://openalex.org/W4312774735","https://openalex.org/W4362653147","https://openalex.org/W4367046983","https://openalex.org/W6600062020","https://openalex.org/W6600129504","https://openalex.org/W6600168703","https://openalex.org/W6600804061","https://openalex.org/W6759238902"],"related_works":["https://openalex.org/W2165912799","https://openalex.org/W2735662278","https://openalex.org/W2382615723","https://openalex.org/W4311804456","https://openalex.org/W1987484445","https://openalex.org/W2623658258","https://openalex.org/W2143413548","https://openalex.org/W4250128246","https://openalex.org/W2933653026","https://openalex.org/W2075445622"],"abstract_inverted_index":{"Domain":[0],"generalization":[1,53,103,252,264],"(DG)":[2],"is":[3,34],"a":[4,45,81,165,194],"prevalent":[5],"problem":[6],"in":[7,83,152],"real-world":[8],"applications,":[9],"which":[10,29,245],"aims":[11],"to":[12,50,68,89,100,109,133],"train":[13],"well-generalized":[14],"models":[15],"for":[16,139,169,204,251],"unseen":[17],"target":[18],"domains":[19,114,242],"by":[20,112,172,233,241],"utilizing":[21],"several":[22],"source":[23],"domains.":[24,78],"Since":[25],"domain":[26,30,58,72,85,148,170,249],"labels,":[27],"i.e.,":[28,74],"each":[31],"data":[32],"point":[33,145],"sampled":[35],"from,":[36],"naturally":[37],"exist,":[38],"most":[39,130,215],"DG":[40,206],"algorithms":[41],"treat":[42],"them":[43],"as":[44],"kind":[46],"of":[47,71,136],"supervision":[48,65],"information":[49],"improve":[51],"the":[52,56,63,69,75,98,102,117,122,129,134,137,156,180,205,209,214,224,237],"performance.":[54,265],"However,":[55],"original":[57,93],"labels":[59,250],"may":[60,86,126],"not":[61,127],"be":[62,87,97,128],"optimal":[64],"signal":[66],"due":[67,132],"lack":[70,135],"heterogeneity,":[73],"diversity":[76],"among":[77],"For":[79],"example,":[80],"sample":[82],"one":[84],"closer":[88],"another":[90],"domain,":[91],"its":[92],"label":[94],"thus":[95,192],"can":[96],"noise":[99],"disturb":[101],"learning.":[104,253],"Although":[105],"some":[106],"methods":[107],"try":[108],"solve":[110],"it":[111],"re-dividing":[113],"and":[115,186,243,261],"applying":[116],"newly":[118],"generated":[119,248],"dividing":[120,217],"pattern,":[121],"pattern":[123,218],"they":[124],"choose":[125],"heterogeneous":[131,216],"metric":[138,168],"heterogeneity.":[140],"In":[141,208,223],"this":[142],"paper,":[143],"we":[144,163,178,212,227],"out":[146],"that":[147],"heterogeneity":[149,171,185,260],"mainly":[150],"lies":[151],"variant":[153,175],"features":[154],"under":[155],"invariant":[157],"learning":[158,166,174,232],"framework.":[159],"With":[160],"contrastive":[161,221,231],"learning,":[162],"propose":[164,193],"potential-guided":[167],"promoting":[173],"features.":[176],"Then":[177],"notice":[179],"differences":[181],"between":[182],"seeking":[183],"variance-based":[184],"training":[187],"invariance-based":[188],"generalizable":[189],"model.":[190],"We":[191],"novel":[195],"method":[196],"called":[197],"H":[198],"eterogeneity-based":[199],"Two-stage":[200],"Contrastive":[201],"Learning":[202],"(HTCL)":[203],"task.":[207],"first":[210],"stage,":[211,226],"generate":[213],"with":[219,236],"our":[220],"metric.":[222],"second":[225],"employ":[228],"an":[229],"invariance-aimed":[230],"re-building":[234],"pairs":[235],"stable":[238],"relation":[239],"hinted":[240],"classes,":[244],"better":[246,258],"utilizes":[247],"Extensive":[254],"experiments":[255],"show":[256],"HTCL":[257],"digs":[259],"yields":[262],"great":[263]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}
