{"id":"https://openalex.org/W7204841581","doi":"https://doi.org/10.48550/arxiv.2608.27877","title":"Relational Knowledge Distillation Brings DNN Representations Close Enough to Humans to Be Aligned Without Supervision","display_name":"Relational Knowledge Distillation Brings DNN Representations Close Enough to Humans to Be Aligned Without Supervision","publication_year":2026,"publication_date":"2026-08-28","ids":{"openalex":"https://openalex.org/W7204841581","doi":"https://doi.org/10.48550/arxiv.2608.27877"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2608.27877","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.27877","pdf_url":"https://arxiv.org/pdf/2608.27877","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,"listed_in":[],"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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2608.27877","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127576497","display_name":"Yuria Shimizu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shimizu, Yuria","raw_affiliation_strings":["Graduate School of Arts and Sciences , The University of Tokyo , 3-8-1 Komaba , 153-8902 , Meguro , Tokyo , Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Arts and Sciences , The University of Tokyo , 3-8-1 Komaba , 153-8902 , Meguro , Tokyo , Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058971190","display_name":"Soh Takahashi","orcid":"https://orcid.org/0009-0004-4648-2487","observed_orcids":["https://orcid.org/0009-0004-4648-2487"]},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takahashi, Soh","raw_affiliation_strings":["Graduate School of Arts and Sciences , The University of Tokyo , 3-8-1 Komaba , 153-8902 , Meguro , Tokyo , Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Arts and Sciences , The University of Tokyo , 3-8-1 Komaba , 153-8902 , Meguro , Tokyo , Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031377124","display_name":"Takato Horii","orcid":"https://orcid.org/0000-0001-5374-0887","observed_orcids":["https://orcid.org/0000-0001-5374-0887","https://orcid.org/0009-0006-9904-3933"]},"institutions":[{"id":"https://openalex.org/I98285908","display_name":"The University of Osaka","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Horii, Takato","raw_affiliation_strings":["Graduate School of Engineering Science , The University of Osaka , 1-3 Machikaneyama-cho , 560-8531 , Toyonaka , Osaka , Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering Science , The University of Osaka , 1-3 Machikaneyama-cho , 560-8531 , Toyonaka , Osaka , Japan","institution_ids":["https://openalex.org/I98285908"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053543093","display_name":"Masafumi Oizumi","orcid":"https://orcid.org/0000-0001-8802-2607","observed_orcids":["https://orcid.org/0000-0001-8802-2607"]},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Oizumi, Masafumi","raw_affiliation_strings":["Graduate School of Arts and Sciences , The University of Tokyo , 3-8-1 Komaba , 153-8902 , Meguro , Tokyo , Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Arts and Sciences , The University of Tokyo , 3-8-1 Komaba , 153-8902 , Meguro , Tokyo , Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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":0.823199987411499,"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":0.823199987411499,"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":0.09549999982118607,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.010099999606609344,"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/unsupervised-alignment","display_name":"unsupervised alignment","score":0.6600000262260437},{"id":"https://openalex.org/keywords/representational-similarity","display_name":"representational similarity","score":0.6850000023841858},{"id":"https://openalex.org/keywords/fine-grained-alignment","display_name":"fine-grained alignment","score":0.6430000066757202}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.8008999824523926},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.6448000073432922},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6061999797821045},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6055999994277954},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5536999702453613},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5318999886512756},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4936000108718872},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4787999987602234},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4099999964237213},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39969998598098755},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39750000834465027},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.3743000030517578},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3700000047683716},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3465999960899353},{"id":"https://openalex.org/C2776608160","wikidata":"https://www.wikidata.org/wiki/Q4785462","display_name":"Natural (archaeology)","level":2,"score":0.3327000141143799},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.3287000060081482},{"id":"https://openalex.org/C96199812","wikidata":"https://www.wikidata.org/wiki/Q2145290","display_name":"Mental representation","level":3,"score":0.3278999924659729},{"id":"https://openalex.org/C2776960227","wikidata":"https://www.wikidata.org/wiki/Q2586354","display_name":"Knowledge transfer","level":2,"score":0.32440000772476196},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.31520000100135803},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3052000105381012},{"id":"https://openalex.org/C177877439","wikidata":"https://www.wikidata.org/wiki/Q7604413","display_name":"Statistical relational learning","level":3,"score":0.2872999906539917},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2849999964237213},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.25130000710487366},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2608.27877","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.27877","pdf_url":"https://arxiv.org/pdf/2608.27877","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,"listed_in":[],"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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2608.27877","doi":"https://doi.org/10.48550/arxiv.2608.27877","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.27877","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,"listed_in":[],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2608.27877","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.27877","pdf_url":"https://arxiv.org/pdf/2608.27877","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,"listed_in":[],"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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G7369646655","display_name":null,"funder_award_id":"23H04834","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"},{"id":"https://openalex.org/F4320338247","display_name":"Moonshot Research and Development Program","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204841581.pdf","grobid_xml":"https://content.openalex.org/works/W7204841581.grobid-xml"},"referenced_works_count":49,"referenced_works":["https://openalex.org/W1715013381","https://openalex.org/W1821584645","https://openalex.org/W2027842533","https://openalex.org/W2040036684","https://openalex.org/W2046033161","https://openalex.org/W2057069782","https://openalex.org/W2058616551","https://openalex.org/W2102381086","https://openalex.org/W2108598243","https://openalex.org/W2117539524","https://openalex.org/W2134145060","https://openalex.org/W2150593711","https://openalex.org/W2160654481","https://openalex.org/W2176287621","https://openalex.org/W2274405424","https://openalex.org/W2412480261","https://openalex.org/W2762409611","https://openalex.org/W2795373741","https://openalex.org/W2900501824","https://openalex.org/W2949676527","https://openalex.org/W2952760759","https://openalex.org/W2963140444","https://openalex.org/W2969697317","https://openalex.org/W2979826702","https://openalex.org/W2980938713","https://openalex.org/W3003257820","https://openalex.org/W3032815891","https://openalex.org/W3039256091","https://openalex.org/W3092472394","https://openalex.org/W3093096163","https://openalex.org/W3110364767","https://openalex.org/W3159481202","https://openalex.org/W3179888904","https://openalex.org/W3209532394","https://openalex.org/W4226002507","https://openalex.org/W4322494696","https://openalex.org/W4386065512","https://openalex.org/W4403924340","https://openalex.org/W4409417786","https://openalex.org/W4409468728","https://openalex.org/W4415291046","https://openalex.org/W4416204877","https://openalex.org/W6931203431","https://openalex.org/W6931259419","https://openalex.org/W7129494248","https://openalex.org/W7133195915","https://openalex.org/W7133202078","https://openalex.org/W7133220105","https://openalex.org/W7133250524"],"related_works":["https://openalex.org/W4415291046","https://openalex.org/W4416204877","https://openalex.org/W4411543200","https://openalex.org/W7160331938","https://openalex.org/W2167254107","https://openalex.org/W4381245606","https://openalex.org/W7221767288","https://openalex.org/W3043600630","https://openalex.org/W2973337740","https://openalex.org/W7133202078"],"abstract_inverted_index":{"Linking":[0],"the":[1,62,95,110,129,152,181,205,213,231,240],"internal":[2,130],"representations":[3,12,26,67],"of":[4,21,48,65,109,148,207,234],"deep":[5],"neural":[6],"networks":[7],"(DNNs)":[8],"to":[9,30,54,59,100,175,177,239,243],"human":[10,22,31,66,102,241],"mental":[11,32],"is":[13,58,194],"important":[14],"for":[15],"using":[16],"DNNs":[17,160,172,236],"as":[18,202],"computational":[19],"models":[20],"vision.":[23],"Existing":[24],"DNN":[25],"remain":[27],"insufficiently":[28],"similar":[29],"representations,":[33],"which":[34,124],"are":[35,40],"not":[36],"directly":[37,60],"observable":[38],"and":[39,70,98,134],"therefore":[41],"commonly":[42],"measured":[43],"through":[44],"large-scale":[45],"similarity":[46],"judgments":[47],"object":[49],"images.":[50],"A":[51],"natural":[52],"approach":[53],"narrowing":[55],"this":[56,81,185,192],"gap":[57],"transfer":[61,169,227],"relational":[63,168,226],"structure":[64,132,233,242],"into":[68],"DNNs,":[69],"previous":[71],"studies":[72],"have":[73],"reported":[74],"improved":[75],"human-DNN":[76,126,215,246],"representational":[77],"similarity.":[78],"However,":[79],"whether":[80],"improvement":[82,193],"holds":[83],"under":[84],"stricter":[85],"evaluation":[86],"remains":[87,219],"untested":[88],"in":[89,204],"two":[90],"respects:":[91],"fine-grained":[92,137,245],"alignment":[93,247],"at":[94,180],"individual-object":[96,182],"level,":[97],"generalization":[99,142],"a":[101,106,144,197],"embedding":[103],"derived":[104],"from":[105,128,228],"dataset":[107],"independent":[108],"training":[111,153],"data.":[112,154],"Here,":[113],"we":[114],"employ":[115],"an":[116,166],"unsupervised":[117],"comparison":[118],"method,":[119,170],"Gromov-Wasserstein":[120],"optimal":[121],"transport":[122],"(GWOT),":[123],"estimates":[125],"correspondences":[127],"distance":[131],"alone":[133],"thereby":[135],"tests":[136],"alignment.":[138],"We":[139,155,188],"further":[140],"assess":[141],"on":[143,184],"curated":[145],"test":[146,186],"set":[147],"concepts":[149],"non-overlapping":[150],"with":[151,161],"show":[156,190],"that":[157,191,225],"fine-tuning":[158],"pre-trained":[159,235],"Relational":[162],"Knowledge":[163],"Distillation":[164],"(RKD),":[165],"established":[167],"brings":[171,230],"close":[173,237],"enough":[174,238],"humans":[176,229],"be":[178],"aligned":[179],"level":[183],"set.":[187],"also":[189],"driven":[195],"by":[196],"more":[198],"human-like":[199],"global":[200,232],"structure,":[201],"reflected":[203],"ordering":[206],"distances":[208],"among":[209],"coarse":[210],"categories,":[211],"while":[212],"local":[214],"nearest-neighbor":[216],"overlap":[217],"rate":[218],"largely":[220],"unchanged.":[221],"These":[222],"findings":[223],"indicate":[224],"enable":[244],"without":[248],"supervision.":[249]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-09-01T00:00:00"}