{"id":"https://openalex.org/W7160395101","doi":"https://doi.org/10.48550/arxiv.2605.03877","title":"DMGD: Train-Free Dataset Distillation with Semantic-Distribution Matching in Diffusion Models","display_name":"DMGD: Train-Free Dataset Distillation with Semantic-Distribution Matching in Diffusion Models","publication_year":2026,"publication_date":"2026-05-05","ids":{"openalex":"https://openalex.org/W7160395101","doi":"https://doi.org/10.48550/arxiv.2605.03877"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.03877","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03877","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.03877","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135469219","display_name":"Qichao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Qichao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135480605","display_name":"Yunhong Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Yunhong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135489656","display_name":"Hengyuan Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Hengyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135479969","display_name":"Junyi Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Junyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135528539","display_name":"Min Zhang","orcid":"https://orcid.org/0000-0003-4143-836X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Min","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.48420000076293945,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.48420000076293945,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.07779999822378159,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.07129999995231628,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.7696999907493591},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.49140000343322754},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.47760000824928284},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.4494999945163727},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.4108000099658966}],"concepts":[{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.7696999907493591},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7196000218391418},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.49140000343322754},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.47760000824928284},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4629000127315521},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.4494999945163727},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41830000281333923},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.4108000099658966},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3483999967575073},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3458000123500824},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.28630000352859497},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2809999883174896},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.26980000734329224}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.03877","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03877","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":"doi:10.48550/arxiv.2605.03877","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03877","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Dataset":[0],"distillation":[1,53],"enables":[2],"efficient":[3,65],"training":[4],"by":[5,172],"distilling":[6],"the":[7,78,92,116],"information":[8],"of":[9,94,176],"large-scale":[10],"datasets":[11],"into":[12],"significantly":[13],"smaller":[14],"synthetic":[15,95],"datasets.":[16],"Diffusion":[17,60],"based":[18,51,108,130],"paradigms":[19],"have":[20],"emerged":[21],"in":[22],"recent":[23],"years,":[24],"offering":[25],"novel":[26],"perspectives":[27],"for":[28,80,128],"dataset":[29,52],"distillation.":[30],"However,":[31],"they":[32],"typically":[33],"necessitate":[34],"additional":[35,170],"fine-tuning":[36,171],"stages,":[37],"and":[38,54,135,155,179],"effective":[39,142],"guidance":[40,88,145],"mechanisms":[41],"remain":[42],"underexplored.":[43],"To":[44,120],"address":[45],"these":[46],"limitations,":[47],"we":[48,84,102,123],"rethink":[49],"diffusion":[50,129],"propose":[55,85],"a":[56,86],"Dual":[57],"Matching":[58,72,110,134],"Guided":[59],"(DMGD)":[61],"framework,":[62],"centered":[63],"on":[64,152],"training-free":[66,160],"guidance.":[67],"We":[68],"first":[69],"establish":[70],"Semantic":[71],"via":[73],"conditional":[74],"likelihood":[75],"optimization,":[76],"eliminating":[77],"need":[79],"auxiliary":[81],"classifiers.":[82],"Furthermore,":[83],"dynamic":[87],"mechanism":[89],"that":[90,158],"enhances":[91],"diversity":[93],"data":[96],"while":[97],"maintaining":[98],"semantic":[99],"alignment.":[100],"Simultaneously,":[101],"introduce":[103],"an":[104],"optimal":[105],"transport":[106],"(OT)":[107],"Distribution":[109,132],"approach":[111,161],"to":[112],"further":[113],"align":[114],"with":[115,146],"target":[117],"distribution":[118,143],"structure.":[119],"ensure":[121],"efficiency,":[122],"develop":[124],"two":[125],"enhanced":[126],"strategies":[127,140],"framework:":[131],"Approximate":[133],"Greedy":[136],"Progressive":[137],"Matching.":[138],"These":[139],"enable":[141],"matching":[144],"minimal":[147],"computational":[148],"overhead.":[149],"Experimental":[150],"results":[151],"ImageNet-Woof,":[153],"ImageNet-Nette,":[154],"ImageNet-1K":[156],"demonstrate":[157],"our":[159],"achieves":[162],"significant":[163],"improvements,":[164],"outperforming":[165],"state-of-the-art":[166],"(SOTA)":[167],"methods":[168],"requiring":[169],"average":[173],"accuracy":[174],"gains":[175],"2.1%,":[177],"5.4%,":[178],"2.4%,":[180],"respectively.":[181]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-07T00:00:00"}