{"id":"https://openalex.org/W7151404020","doi":"https://doi.org/10.1109/icmla66185.2025.00073","title":"Supervised Contrastive Disentanglement for Classification","display_name":"Supervised Contrastive Disentanglement for Classification","publication_year":2025,"publication_date":"2025-12-03","ids":{"openalex":"https://openalex.org/W7151404020","doi":"https://doi.org/10.1109/icmla66185.2025.00073"},"language":null,"primary_location":{"id":"doi:10.1109/icmla66185.2025.00073","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00073","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","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/A5111068171","display_name":"Joshua Luo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210115001","display_name":"Westminster Schools","ror":"https://ror.org/02ammvg77","country_code":"US","type":"education","lineage":["https://openalex.org/I4210115001"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Joshua Luo","raw_affiliation_strings":["The Westminster Schools,Atlanta,Georgia"],"affiliations":[{"raw_affiliation_string":"The Westminster Schools,Atlanta,Georgia","institution_ids":["https://openalex.org/I4210115001"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133089928","display_name":"Luke Zhong","orcid":null},"institutions":[{"id":"https://openalex.org/I2802320588","display_name":"Germantown Academy","ror":"https://ror.org/01j78w442","country_code":"US","type":"education","lineage":["https://openalex.org/I2802320588"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Luke Zhong","raw_affiliation_strings":["Germantown Friends School,Philadelphia,Pennsylvania"],"affiliations":[{"raw_affiliation_string":"Germantown Friends School,Philadelphia,Pennsylvania","institution_ids":["https://openalex.org/I2802320588"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057749344","display_name":"Feiyang Cai","orcid":"https://orcid.org/0000-0002-1486-0971"},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Feiyang Cai","raw_affiliation_strings":["Clemson University,School of Computing"],"affiliations":[{"raw_affiliation_string":"Clemson University,School of Computing","institution_ids":["https://openalex.org/I8078737"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5133141523","display_name":"Long Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Long Cheng","raw_affiliation_strings":["Clemson University,School of Computing"],"affiliations":[{"raw_affiliation_string":"Clemson University,School of Computing","institution_ids":["https://openalex.org/I8078737"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5111068171"],"corresponding_institution_ids":["https://openalex.org/I4210115001"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.74912973,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"492","last_page":"499"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.1607999950647354,"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"}},"topics":[{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.1607999950647354,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.07440000027418137,"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":0.06769999861717224,"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/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34310001134872437},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.31209999322891235},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.25189998745918274},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.24369999766349792},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.21529999375343323}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5715000033378601},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5407999753952026},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34310001134872437},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.31209999322891235},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2863999903202057},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.25189998745918274},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.24369999766349792},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24210000038146973},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.21529999375343323},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.21469999849796295}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmla66185.2025.00073","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00073","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7139683365821838,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2138621090","https://openalex.org/W2163922914","https://openalex.org/W2194775991","https://openalex.org/W2772723798","https://openalex.org/W2906298613","https://openalex.org/W2919115771","https://openalex.org/W2949736877","https://openalex.org/W2962898354","https://openalex.org/W2992308087","https://openalex.org/W3035524453","https://openalex.org/W3035682985","https://openalex.org/W3160329607","https://openalex.org/W4307823382","https://openalex.org/W4404200101"],"related_works":[],"abstract_inverted_index":{"Both":[0],"supervised":[1,9,51],"contrastive":[2,52],"learning":[3,10,53],"and":[4,54,65,98,111,129],"traditional":[5],"cross":[6,55],"entropy":[7,56],"based":[8],"methods":[11,24],"have":[12],"demonstrated":[13],"strong":[14],"performance":[15,31],"in":[16,138],"classification":[17,33,137],"tasks.":[18,34],"The":[19],"combination":[20],"of":[21,93,121,133],"those":[22],"two":[23],"has":[25],"the":[26,99,124,131],"potential":[27],"to":[28,57,72,83],"achieve":[29],"better":[30],"for":[32,43,135],"In":[35],"this":[36],"paper,":[37],"we":[38],"introduce":[39],"Supervised":[40],"Contrastive":[41],"Disentanglement":[42],"Classification":[44],"(CoDiC),":[45],"a":[46,90,118],"novel":[47],"method":[48,126],"that":[49],"combines":[50],"explicitly":[58],"disentangle":[59],"latent":[60],"features":[61],"into":[62],"class":[63,74],"logits":[64],"instance-specific":[66],"embeddings.":[67],"This":[68],"disentanglement":[69,134],"enables":[70],"CoDiC":[71,88,103,116],"isolate":[73],"information":[75],"while":[76],"suppressing":[77],"spurious":[78],"or":[79],"instance-dependent":[80],"variations,":[81],"leading":[82],"enhanced":[84],"performance.":[85],"We":[86],"evaluate":[87],"across":[89],"diverse":[91],"set":[92],"datasets,":[94],"including":[95,108],"CIFAR-10,":[96],"CIFAR-100,":[97,115],"histopathology":[100],"dataset":[101],"CAMELYON16.":[102],"consistently":[104],"outperforms":[105],"baseline":[106],"methods,":[107],"cross-entropy,":[109],"SimCLR,":[110],"SupCon.":[112],"Notably,":[113],"on":[114],"achieves":[117],"top-1":[119],"accuracy":[120],"80.1%,":[122],"outperforming":[123],"next-best":[125],"by":[127],"3.6%,":[128],"demonstrating":[130],"effectiveness":[132],"robust":[136],"different":[139],"domains.":[140]},"counts_by_year":[],"updated_date":"2026-04-09T06:08:40.794217","created_date":"2026-04-08T00:00:00"}
