{"id":"https://openalex.org/W7124166272","doi":"https://doi.org/10.48550/arxiv.2601.08024","title":"A Highly Efficient Diversity-based Input Selection for DNN Improvement Using VLMs","display_name":"A Highly Efficient Diversity-based Input Selection for DNN Improvement Using VLMs","publication_year":2026,"publication_date":"2026-01-12","ids":{"openalex":"https://openalex.org/W7124166272","doi":"https://doi.org/10.48550/arxiv.2601.08024"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.08024","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.08024","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.2601.08024","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092657440","display_name":"Amin Abbasishahkoo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abbasishahkoo, Amin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072142047","display_name":"Mahboubeh Dadkhah","orcid":"https://orcid.org/0000-0002-0436-8369"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dadkhah, Mahboubeh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5078533117","display_name":"Lionel Briand","orcid":"https://orcid.org/0000-0002-1393-1010"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Briand, Lionel","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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.2842000126838684,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.2842000126838684,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.21570000052452087,"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":0.09279999881982803,"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/selection","display_name":"Selection (genetic algorithm)","score":0.7979999780654907},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.738099992275238},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.579200029373169},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5464000105857849},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.511900007724762},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.47429999709129333},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.4650999903678894}],"concepts":[{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.7979999780654907},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.738099992275238},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7305999994277954},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.579200029373169},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5464000105857849},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5296000242233276},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.511900007724762},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4943000078201294},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.47429999709129333},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.4650999903678894},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4546000063419342},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.43619999289512634},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31540000438690186},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.29829999804496765},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2872999906539917},{"id":"https://openalex.org/C2775973920","wikidata":"https://www.wikidata.org/wiki/Q3252726","display_name":"Selection algorithm","level":3,"score":0.27619999647140503},{"id":"https://openalex.org/C149629883","wikidata":"https://www.wikidata.org/wiki/Q660926","display_name":"Fraction (chemistry)","level":2,"score":0.26179999113082886}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.08024","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.08024","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.2601.08024","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.08024","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Maintaining":[0],"or":[1],"improving":[2],"the":[3,52,170,183,187,219,225],"performance":[4],"of":[5,51,119,152,200,224],"Deep":[6],"Neural":[7],"Networks":[8],"(DNNs)":[9],"through":[10],"fine-tuning":[11],"requires":[12],"labeling":[13],"newly":[14],"collected":[15],"inputs,":[16],"a":[17,85,103,117,129,139,145,149],"process":[18],"that":[19,92,100,134,169],"is":[20,49],"often":[21,62],"costly":[22],"and":[23,65,159,221,239],"time-consuming.":[24],"To":[25,76],"alleviate":[26],"this":[27,57,78,125],"problem,":[28],"input":[29,70,131,155,179,210,241],"selection":[30,48,132,157,164,172,180,189,195,242],"approaches":[31,55],"have":[32],"been":[33],"developed":[34],"in":[35,237],"recent":[36],"years":[37],"to":[38,181,198,230],"identify":[39],"small,":[40],"yet":[41],"highly":[42,86,192],"informative":[43],"subsets":[44],"for":[45,56,68,89],"labeling.":[46],"Diversity-based":[47],"one":[50],"most":[53,161],"effective":[54,162],"purpose.":[58],"However,":[59],"they":[60],"are":[61],"computationally":[63],"intensive":[64],"lack":[66],"scalability":[67,236],"large":[69],"sets,":[71,156],"limiting":[72],"their":[73],"practical":[74],"applicability.":[75],"address":[77],"challenge,":[79],"we":[80,127],"introduce":[81],"Concept-Based":[82],"Diversity":[83,108],"(CBD),":[84],"efficient":[87],"metric":[88],"image":[90],"inputs":[91],"leverages":[93],"Vision-Language":[94],"Models":[95],"(VLM).":[96],"Our":[97],"results":[98,167,215],"show":[99],"CBD":[101,136],"exhibits":[102],"strong":[104],"correlation":[105],"with":[106,137],"Geometric":[107],"(GD),":[109],"an":[110],"established":[111],"diversity":[112],"metric,":[113],"while":[114],"requiring":[115,194],"only":[116,218],"fraction":[118],"its":[120,235],"computation":[121],"time.":[122],"Building":[123],"on":[124,208],"finding,":[126],"propose":[128],"hybrid":[130,231],"approach":[133,190],"combines":[135],"Margin,":[138,206],"simple":[140,201],"uncertainty":[141],"metric.":[142],"We":[143],"conduct":[144],"comprehensive":[146],"evaluation":[147],"across":[148],"diverse":[150],"set":[151],"DNN":[153,184],"models,":[154],"budgets,":[158],"five":[160],"state-of-the-art":[163],"baselines.":[165],"The":[166],"demonstrate":[168],"CBD-based":[171,188,226],"consistently":[173],"outperforms":[174],"all":[175],"baselines":[176],"at":[177],"guiding":[178],"improve":[182],"model.":[185],"Furthermore,":[186],"remains":[191],"efficient,":[193],"times":[196],"close":[197],"those":[199],"uncertainty-based":[202],"methods":[203],"such":[204],"as":[205],"even":[207],"larger":[209],"sets":[211],"like":[212],"ImageNet.":[213],"These":[214],"confirm":[216],"not":[217],"effectiveness":[220],"computational":[222],"advantage":[223],"approach,":[227],"particularly":[228],"compared":[229],"baselines,":[232],"but":[233],"also":[234],"repetitive":[238],"extensive":[240],"scenarios.":[243]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-01-15T00:00:00"}
