{"id":"https://openalex.org/W7160323035","doi":"https://doi.org/10.1109/wacv61042.2026.00510","title":"Concord: Concept-Informed Diffusion for Dataset Distillation","display_name":"Concord: Concept-Informed Diffusion for Dataset Distillation","publication_year":2026,"publication_date":"2026-03-06","ids":{"openalex":"https://openalex.org/W7160323035","doi":"https://doi.org/10.1109/wacv61042.2026.00510"},"language":null,"primary_location":{"id":"doi:10.1109/wacv61042.2026.00510","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00510","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5044808270","display_name":"Jianyang Gu","orcid":"https://orcid.org/0000-0002-4060-7427"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jianyang Gu","raw_affiliation_strings":["The Ohio State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Ohio State University","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100342615","display_name":"Haonan Wang","orcid":"https://orcid.org/0009-0006-6963-8987"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Haonan Wang","raw_affiliation_strings":["National University of Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135351987","display_name":"Ruoxi Jia","orcid":null},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ruoxi Jia","raw_affiliation_strings":["Virginia Tech"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Virginia Tech","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049755417","display_name":"Saeed Vahidian","orcid":"https://orcid.org/0000-0002-1258-0343"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Saeed Vahidian","raw_affiliation_strings":["Duke University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Duke University","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135361804","display_name":"Vyacheslav Kungurtsev","orcid":null},"institutions":[{"id":"https://openalex.org/I44504214","display_name":"Czech Technical University in Prague","ror":"https://ror.org/03kqpb082","country_code":"CZ","type":"education","lineage":["https://openalex.org/I44504214"]}],"countries":["CZ"],"is_corresponding":false,"raw_author_name":"Vyacheslav Kungurtsev","raw_affiliation_strings":["Czech Technical University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Czech Technical University","institution_ids":["https://openalex.org/I44504214"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135393190","display_name":"Wei Jiang","orcid":null},"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":"Wei Jiang","raw_affiliation_strings":["Zhejiang University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5135300982","display_name":"Yiran Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiran Chen","raw_affiliation_strings":["Duke University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Duke University","institution_ids":["https://openalex.org/I170897317"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.46097989,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5258","last_page":"5268"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.1282999962568283,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.1282999962568283,"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":0.1216999962925911,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.06859999895095825,"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/diffusion","display_name":"Diffusion","score":0.44749999046325684},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.40400001406669617},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.25760000944137573},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.2484000027179718}],"concepts":[{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.44749999046325684},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.40400001406669617},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.2992999851703644},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2782000005245209},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.2766999900341034},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.27619999647140503},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","level":1,"score":0.26089999079704285},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.2484000027179718},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.24240000545978546}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv61042.2026.00510","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00510","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W12634471","https://openalex.org/W1590636096","https://openalex.org/W1997865285","https://openalex.org/W2108598243","https://openalex.org/W2194775991","https://openalex.org/W2963108767","https://openalex.org/W2992308087","https://openalex.org/W3094502228","https://openalex.org/W4312412605","https://openalex.org/W4312480718","https://openalex.org/W4312740349","https://openalex.org/W4312933868","https://openalex.org/W4313156423","https://openalex.org/W4319300193","https://openalex.org/W4386065613","https://openalex.org/W4386072282","https://openalex.org/W4386075990","https://openalex.org/W4387490658","https://openalex.org/W4390871903","https://openalex.org/W4390872297","https://openalex.org/W4390872541","https://openalex.org/W4393153182","https://openalex.org/W4396832909","https://openalex.org/W4400118952","https://openalex.org/W4401043057","https://openalex.org/W4402660160","https://openalex.org/W4402727287","https://openalex.org/W4402727721","https://openalex.org/W4402753490","https://openalex.org/W4402772294","https://openalex.org/W4410914987","https://openalex.org/W7133208271"],"related_works":[],"abstract_inverted_index":{"Dataset":[0],"distillation":[1,46],"(DD)":[2],"has":[3],"witnessed":[4],"significant":[5],"progress":[6],"in":[7,86],"creating":[8],"small":[9],"datasets":[10],"that":[11],"encapsulate":[12],"rich":[13],"information":[14],"from":[15,53],"large":[16,99],"original":[17],"ones.":[18],"Particularly,":[19],"methods":[20,47],"based":[21,118],"on":[22,119,156,169],"generative":[23],"priors":[24],"show":[25],"promising":[26],"performance":[27,168],"while":[28],"maintaining":[29],"computational":[30],"efficiency":[31],"and":[32,113,127,147,171],"cross-architecture":[33],"generalization.":[34],"However,":[35],"the":[36,50,54,57,65,81,95,124,145,150,161],"generation":[37],"process":[38,126],"lacks":[39],"explicit":[40],"controllability":[41,146],"for":[42,108],"each":[43],"sample.":[44],"Previous":[45],"primarily":[48],"match":[49],"real":[51],"distribution":[52],"perspective":[55],"of":[56,98,149,163],"entire":[58],"dataset,":[59],"whereas":[60],"overlooking":[61],"concept":[62,96],"completeness":[63],"at":[64,176],"instance":[66],"level.":[67],"The":[68],"missing":[69],"or":[70],"incorrectly":[71],"represented":[72],"object":[73,130],"details":[74],"cannot":[75],"be":[76,135],"efficiently":[77],"compensated":[78],"due":[79],"to":[80,103,122,137,142],"constrained":[82],"sample":[83],"amount":[84],"typical":[85],"DD":[87,140],"settings.":[88],"To":[89],"this":[90],"end,":[91],"we":[92],"propose":[93],"incorporating":[94],"understanding":[97],"language":[100],"models":[101],"(LLMs)":[102],"perform":[104],"Concept-Informed":[105],"Diffusion":[106],"(Concord)":[107],"dataset":[109],"distillation.":[110],"Specifically,":[111],"distinguishable":[112],"fine-grained":[114],"concepts":[115,133],"are":[116],"retrieved":[117],"category":[120],"labels":[121],"inform":[123],"denoising":[125],"refine":[128],"essential":[129],"details.":[131],"These":[132],"can":[134],"applied":[136],"any":[138],"diffusion-based":[139],"framework":[141],"enhance":[143],"both":[144],"interpretability":[148],"distilled":[151],"image":[152],"generation,":[153],"without":[154],"relying":[155],"pre-trained":[157],"classifiers.":[158],"We":[159],"demonstrate":[160],"efficacy":[162],"Concord":[164],"by":[165],"achieving":[166],"state-of-the-art":[167],"ImageNet-1K":[170],"subsets.":[172],"Code":[173],"is":[174],"released":[175],"Concord.":[177]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-05-06T00:00:00"}
