{"id":"https://openalex.org/W7106652241","doi":"https://doi.org/10.48550/arxiv.2511.18826","title":"Uncertainty-Aware Dual-Student Knowledge Distillation for Efficient Image Classification","display_name":"Uncertainty-Aware Dual-Student Knowledge Distillation for Efficient Image Classification","publication_year":2025,"publication_date":"2025-11-24","ids":{"openalex":"https://openalex.org/W7106652241","doi":"https://doi.org/10.48550/arxiv.2511.18826"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2511.18826","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.18826","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2511.18826","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Gore, Aakash","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gore, Aakash","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Dey, Anoushka","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dey, Anoushka","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Mishra, Aryan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mishra, Aryan","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/T10036","display_name":"Advanced Neural Network Applications","score":0.5554999709129333,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.5554999709129333,"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":0.06109999865293503,"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":0.0560000017285347,"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/distillation","display_name":"Distillation","score":0.8787999749183655},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.6791999936103821},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4205000102519989},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.414900004863739},{"id":"https://openalex.org/keywords/knowledge-engineering","display_name":"Knowledge engineering","score":0.37630000710487366},{"id":"https://openalex.org/keywords/knowledge-transfer","display_name":"Knowledge transfer","score":0.3580999970436096}],"concepts":[{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.8787999749183655},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6844000220298767},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.6791999936103821},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6037999987602234},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5748999714851379},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4205000102519989},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.414900004863739},{"id":"https://openalex.org/C84685590","wikidata":"https://www.wikidata.org/wiki/Q1540472","display_name":"Knowledge engineering","level":2,"score":0.37630000710487366},{"id":"https://openalex.org/C2776960227","wikidata":"https://www.wikidata.org/wiki/Q2586354","display_name":"Knowledge transfer","level":2,"score":0.3580999970436096},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.32739999890327454},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.3179999887943268},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31380000710487366},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3077999949455261},{"id":"https://openalex.org/C2777220311","wikidata":"https://www.wikidata.org/wiki/Q6423340","display_name":"Knowledge acquisition","level":2,"score":0.28870001435279846},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.262800008058548}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2511.18826","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.18826","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2511.18826","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.18826","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.715948760509491,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Knowledge":[0],"distillation":[1,27,49,100,124],"has":[2],"emerged":[3],"as":[4],"a":[5,63],"powerful":[6],"technique":[7],"for":[8],"model":[9],"compression,":[10],"enabling":[11],"the":[12,36,79],"transfer":[13],"of":[14,35,116],"knowledge":[15,26,48,99],"from":[16,77],"large":[17],"teacher":[18,31,53,80],"networks":[19],"to":[20,56,97],"compact":[21],"student":[22,59,69],"models.":[23],"However,":[24],"traditional":[25,122],"methods":[28],"treat":[29],"all":[30],"predictions":[32],"equally,":[33],"regardless":[34],"teacher's":[37],"confidence":[38],"in":[39],"those":[40],"predictions.":[41],"This":[42],"paper":[43],"proposes":[44],"an":[45],"uncertainty-aware":[46],"dual-student":[47],"framework":[50],"that":[51,90],"leverages":[52],"prediction":[54],"uncertainty":[55],"selectively":[57],"guide":[58],"learning.":[60],"We":[61],"introduce":[62],"peer-learning":[64],"mechanism":[65],"where":[66],"two":[67],"heterogeneous":[68],"architectures,":[70],"specifically":[71],"ResNet-18":[72,103],"and":[73,82,108,118],"MobileNetV2,":[74],"learn":[75],"collaboratively":[76],"both":[78],"network":[81],"each":[83],"other.":[84],"Experimental":[85],"results":[86],"on":[87],"ImageNet-100":[88],"demonstrate":[89],"our":[91],"approach":[92],"achieves":[93],"superior":[94],"performance":[95],"compared":[96],"baseline":[98],"methods,":[101],"with":[102],"achieving":[104,110],"83.84\\%":[105],"top-1":[106,112],"accuracy":[107],"MobileNetV2":[109],"81.46\\%":[111],"accuracy,":[113],"representing":[114],"improvements":[115],"2.04\\%":[117],"0.92\\%":[119],"respectively":[120],"over":[121],"single-student":[123],"approaches.":[125]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-11-27T00:00:00"}
