{"id":"https://openalex.org/W7141716490","doi":"https://doi.org/10.48550/arxiv.2603.25144","title":"FD$^2$: A Dedicated Framework for Fine-Grained Dataset Distillation","display_name":"FD$^2$: A Dedicated Framework for Fine-Grained Dataset Distillation","publication_year":2026,"publication_date":"2026-03-26","ids":{"openalex":"https://openalex.org/W7141716490","doi":"https://doi.org/10.48550/arxiv.2603.25144"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.25144","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25144","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.25144","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130748874","display_name":"Hongxu Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ma, Hongxu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130772363","display_name":"Guang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Guang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130760265","display_name":"Shijie Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Shijie","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130773127","display_name":"Dongzhan Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Dongzhan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015703381","display_name":"Baoli Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Baoli","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130808138","display_name":"Takahiro Ogawa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ogawa, Takahiro","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130811754","display_name":"Miki Haseyama","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haseyama, Miki","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130786250","display_name":"Zhihui Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zhihui","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5130748874"],"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.3756999969482422,"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":0.3756999969482422,"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":0.2815000116825104,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.1404000073671341,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.8715000152587891},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.652400016784668},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.6377000212669373},{"id":"https://openalex.org/keywords/counterfactual-thinking","display_name":"Counterfactual thinking","score":0.6011999845504761},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.5953999757766724},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5860999822616577},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5819000005722046},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5430999994277954}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8715000152587891},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6600000262260437},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.652400016784668},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.6377000212669373},{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.6011999845504761},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.5953999757766724},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5860999822616577},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5819000005722046},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5430999994277954},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5425000190734863},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.516700029373169},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4198000133037567},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41760000586509705},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38029998540878296},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.36419999599456787},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3562999963760376},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.35339999198913574},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.34610000252723694},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.28299999237060547},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.2727999985218048},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.25144","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25144","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.25144","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25144","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":"article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7626835107803345,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Dataset":[0,110],"distillation":[1],"(DD)":[2],"compresses":[3],"a":[4,9,59,105,136,151],"large":[5,74],"training":[6,16],"set":[7],"into":[8,35],"small":[10],"synthetic":[11],"set,":[12],"reducing":[13],"storage":[14],"and":[15,18,39,52,81,94,116,150,163,174],"cost,":[17],"has":[19],"shown":[20],"strong":[21,181],"results":[22],"on":[23,48,160],"general":[24,164],"benchmarks.":[25],"Decoupled":[26],"DD":[27,173],"further":[28],"improves":[29,175],"efficiency":[30],"by":[31],"splitting":[32],"the":[33,87,99],"pipeline":[34],"pretraining,":[36,123],"sample":[37,142],"distillation,":[38,135],"soft-label":[40],"generation.":[41],"However,":[42],"existing":[43],"decoupled":[44,172],"methods":[45],"largely":[46],"rely":[47],"coarse":[49],"class-label":[50],"supervision":[51],"optimize":[53],"samples":[54,70],"within":[55,86],"each":[56,141],"class":[57,132,145],"in":[58,177],"nearly":[60],"identical":[61],"manner.":[62],"On":[63],"fine-grained":[64,118,137,162],"datasets,":[65],"this":[66],"often":[67],"yields":[68],"distilled":[69],"that":[71,167],"(i)":[72],"retain":[73],"intra-class":[75],"variation":[76],"with":[77,143,171],"subtle":[78],"inter-class":[79],"differences":[80],"(ii)":[82],"become":[83],"overly":[84],"similar":[85],"same":[88],"class,":[89],"limiting":[90],"localized":[91],"discriminative":[92,114,128],"cues":[93],"hurting":[95],"recognition.":[96],"To":[97],"solve":[98],"above-mentioned":[100],"problems,":[101],"we":[102],"propose":[103],"FD$^{2}$,":[104],"dedicated":[106],"framework":[107],"for":[108,120],"Fine-grained":[109],"Distillation.":[111],"FD$^{2}$":[112,168],"localizes":[113],"regions":[115],"constructs":[117],"representations":[119,129],"distillation.":[121],"During":[122,134],"counterfactual":[124],"attention":[125,155],"learning":[126],"aggregates":[127],"to":[130],"update":[131],"prototypes.":[133],"characteristic":[138],"constraint":[139,153],"aligns":[140],"its":[144],"prototype":[146],"while":[147],"repelling":[148],"others,":[149],"similarity":[152],"diversifies":[154],"across":[156],"same-class":[157],"samples.":[158],"Experiments":[159],"multiple":[161],"datasets":[165],"show":[166],"integrates":[169],"seamlessly":[170],"performance":[176],"most":[178],"settings,":[179],"indicating":[180],"transferability.":[182]},"counts_by_year":[],"updated_date":"2026-03-28T06:16:51.555046","created_date":"2026-03-28T00:00:00"}
