{"id":"https://openalex.org/W4417161993","doi":"https://doi.org/10.1109/iccv51701.2025.00275","title":"CaliMatch: Adaptive Calibration for Improving Safe Semi-Supervised Learning","display_name":"CaliMatch: Adaptive Calibration for Improving Safe Semi-Supervised Learning","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4417161993","doi":"https://doi.org/10.1109/iccv51701.2025.00275"},"language":"en","primary_location":{"id":"doi:10.1109/iccv51701.2025.00275","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51701.2025.00275","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2508.00922","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045914271","display_name":"Jinsoo Bae","orcid":"https://orcid.org/0000-0002-4317-3636"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jinsoo Bae","raw_affiliation_strings":["Korea University,Seoul,Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Korea University,Seoul,Republic of Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058258354","display_name":"Seoung Bum Kim","orcid":"https://orcid.org/0000-0002-2205-8516"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seoung Bum Kim","raw_affiliation_strings":["Korea University,Seoul,Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Korea University,Seoul,Republic of Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028207573","display_name":"Hyungrok Do","orcid":"https://orcid.org/0000-0001-5317-6809"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hyungrok Do","raw_affiliation_strings":["NYU Grossman School of Medicine,New York,NY,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NYU Grossman School of Medicine,New York,NY,USA","institution_ids":["https://openalex.org/I57206974"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2867","last_page":"2876"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9501000046730042,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9501000046730042,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.013299999758601189,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.007600000128149986,"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/smoothing","display_name":"Smoothing","score":0.7240999937057495},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.646399974822998},{"id":"https://openalex.org/keywords/overconfidence-effect","display_name":"Overconfidence effect","score":0.583899974822998},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.5049999952316284},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5002999901771545},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.47279998660087585},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.4650000035762787}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7408000230789185},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.7240999937057495},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7073000073432922},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.646399974822998},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6276999711990356},{"id":"https://openalex.org/C51110983","wikidata":"https://www.wikidata.org/wiki/Q16503490","display_name":"Overconfidence effect","level":2,"score":0.583899974822998},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.5049999952316284},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5002999901771545},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.47279998660087585},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.4650000035762787},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4009000062942505},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.37369999289512634},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3450999855995178},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3197000026702881},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2874000072479248},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.28380000591278076},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.2517000138759613}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/iccv51701.2025.00275","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51701.2025.00275","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2508.00922","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.00922","pdf_url":"https://arxiv.org/pdf/2508.00922","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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"},{"id":"doi:10.48550/arxiv.2508.00922","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2508.00922","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2508.00922","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.00922","pdf_url":"https://arxiv.org/pdf/2508.00922","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8411411861","display_name":null,"funder_award_id":"RS-202200144190","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Semi-supervised":[0],"learning":[1,12],"(SSL)":[2],"uses":[3],"unlabeled":[4,33],"data":[5,16],"to":[6,50,79,111,127],"improve":[7],"the":[8,25,32,43,67,105,108,125,130,144,148,151,172],"performance":[9],"of":[10,84,146],"machine":[11],"models":[13],"when":[14],"labeled":[15,44],"is":[17,154],"scarce.":[18],"However,":[19,66],"its":[20],"real-world":[21],"applications":[22],"often":[23],"face":[24],"label":[26,118],"distribution":[27],"mismatch":[28],"problem,":[29],"in":[30,74,87,156,175],"which":[31,102,123],"dataset":[34],"includes":[35],"instances":[36],"whose":[37],"ground-truth":[38],"labels":[39],"are":[40],"absent":[41],"from":[42,72],"training":[45],"dataset.":[46],"Recent":[47],"studies,":[48],"referred":[49],"as":[51],"safe":[52,113,157,176],"SSL,":[53],"have":[54],"addressed":[55],"this":[56],"issue":[57],"by":[58],"using":[59],"both":[60,104,147],"classification":[61],"and":[62,107,120,150,166],"out-of-distribution":[63],"(OOD)":[64],"detection.":[65,92],"existing":[68,173],"methods":[69,174],"may":[70],"suffer":[71],"overconfidence":[73],"deep":[75],"neural":[76],"networks,":[77],"leading":[78],"increased":[80],"SSL":[81,177],"errors":[82],"because":[83],"high":[85],"confidence":[86],"incorrect":[88],"pseudo-labels":[89],"or":[90],"OOD":[91,109,152],"To":[93],"address":[94],"this,":[95],"we":[96],"propose":[97],"a":[98,138],"novel":[99],"method,":[100],"CaliMatch,":[101],"calibrates":[103],"classifier":[106,149],"detector":[110,153],"foster":[112],"SSL.":[114,158],"CaliMatch":[115,170],"presents":[116],"adaptive":[117],"smoothing":[119,131],"temperature":[121],"scaling,":[122],"eliminates":[124],"need":[126],"manually":[128],"tune":[129],"degree":[132],"for":[133,141],"effective":[134],"calibration.":[135],"We":[136],"give":[137],"theoretical":[139],"justification":[140],"why":[142],"improving":[143],"calibration":[145],"crucial":[155],"Extensive":[159],"evaluations":[160],"on":[161],"CIFAR-10,":[162],"CIFAR-100,":[163],"SVHN,":[164],"TinyImageNet,":[165],"ImageNet":[167],"demonstrate":[168],"that":[169],"outperforms":[171],"tasks.":[178]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
