{"id":"https://openalex.org/W7131423806","doi":"https://doi.org/10.48550/arxiv.2602.20114","title":"Benchmarking Unlearning for Vision Transformers","display_name":"Benchmarking Unlearning for Vision Transformers","publication_year":2026,"publication_date":"2026-02-23","ids":{"openalex":"https://openalex.org/W7131423806","doi":"https://doi.org/10.48550/arxiv.2602.20114"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.20114","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.20114","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2602.20114","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126723111","display_name":"Kairan Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhao, Kairan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126701430","display_name":"Iurie Luca","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luca, Iurie","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126802125","display_name":"Peter Triantafillou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Triantafillou, Peter","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5126723111"],"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":0.41119998693466187,"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":0.41119998693466187,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.08169999718666077,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.04430000111460686,"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/benchmarking","display_name":"Benchmarking","score":0.9656999707221985},{"id":"https://openalex.org/keywords/memorization","display_name":"Memorization","score":0.6751000285148621},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5593000054359436},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5515999794006348},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4507000148296356},{"id":"https://openalex.org/keywords/tact","display_name":"Tact","score":0.3276999890804291}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.9656999707221985},{"id":"https://openalex.org/C30038468","wikidata":"https://www.wikidata.org/wiki/Q4354775","display_name":"Memorization","level":2,"score":0.6751000285148621},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5971999764442444},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5593000054359436},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5515999794006348},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5059000253677368},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4984000027179718},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4507000148296356},{"id":"https://openalex.org/C2778833722","wikidata":"https://www.wikidata.org/wiki/Q28457372","display_name":"Tact","level":2,"score":0.3276999890804291},{"id":"https://openalex.org/C141571065","wikidata":"https://www.wikidata.org/wiki/Q1771949","display_name":"Performance measurement","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.3109999895095825},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3102000057697296},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2987000048160553},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.29019999504089355},{"id":"https://openalex.org/C13736549","wikidata":"https://www.wikidata.org/wiki/Q4489420","display_name":"Industrial engineering","level":1,"score":0.2842000126838684},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2727000117301941},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2623000144958496},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.25699999928474426},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.2540000081062317}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.20114","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.20114","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2602.20114","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.20114","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"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":{"Research":[0],"in":[1,88,246],"machine":[2],"unlearning":[3,133],"(MU)":[4],"has":[5,34,54,150],"gained":[6],"strong":[7,44],"momentum:":[8],"MU":[9,49,63,85,117,140,228],"is":[10,78],"now":[11],"widely":[12],"regarded":[13],"as":[14,43],"a":[15,215,250],"critical":[16],"capability":[17],"for":[18,30,51,74,125,233],"building":[19],"safe":[20],"and":[21,70,93,95,113,127,131,176,207,221],"fair":[22],"AI.":[23],"In":[24],"parallel,":[25],"research":[26,50],"into":[27],"transformer":[28],"architectures":[29],"computer":[31],"vision":[32,52],"tasks":[33,53],"been":[35,151],"highly":[36],"successful:":[37],"Increasingly,":[38],"Vision":[39],"Transformers":[40],"(VTs)":[41],"emerge":[42],"alternatives":[45],"to":[46,81,106,120,154,174],"CNNs.":[47],"Yet,":[48],"largely":[55],"centered":[56],"on":[57,138,184,203,208,230,240],"CNNs,":[58,71,175],"not":[59],"VTs.":[60,75,231],"While":[61],"benchmarking":[62,84,139,216],"efforts":[64],"have":[65],"addressed":[66],"LLMs,":[67],"diffusion":[68],"models,":[69],"none":[72],"exist":[73],"This":[76],"work":[77,100,166,213,245],"the":[79,108,157,165,178,234],"first":[80,235],"attempt":[82],"this,":[83],"algorithm":[86],"performance":[87,158,253],"different":[89,97,103,116,123,181],"VT":[90,247],"families":[91],"(ViT":[92],"Swin-T)":[94],"at":[96],"capacities.":[98],"The":[99,186],"employs":[101],"(i)":[102],"datasets,":[104],"selected":[105,119],"assess":[107],"impacts":[109],"of":[110,159,180,197,224],"dataset":[111],"scale":[112],"complexity;":[114],"(ii)":[115],"algorithms,":[118],"represent":[121],"fundamentally":[122],"approaches":[124],"MU;":[126],"(iii)":[128],"both":[129],"single-shot":[130],"continual":[132],"protocols.":[134],"Additionally,":[135],"it":[136,237],"focuses":[137],"algorithms":[141,229,244],"that":[142,192],"leverage":[143],"training":[144,171],"data":[145,172,206],"memorization,":[146],"since":[147],"leveraging":[148],"memorization":[149,182],"recently":[152],"discovered":[153],"significantly":[155],"improve":[156],"previously":[160],"SOTA":[161],"algorithms.":[162],"En":[163],"route,":[164],"characterizes":[167],"how":[168,241],"VTs":[169],"memorize":[170],"relative":[173],"assesses":[177],"impact":[179],"proxies":[183],"performance.":[185],"benchmark":[187],"uses":[188],"unified":[189],"evaluation":[190],"metrics":[191],"capture":[193],"two":[194],"complementary":[195],"notions":[196],"forget":[198],"quality":[199],"along":[200],"with":[201],"accuracy":[202],"unseen":[204],"(test)":[205],"retained":[209],"data.":[210],"Overall,":[211],"this":[212],"offers":[214],"basis,":[217],"enabling":[218],"reproducible,":[219],"fair,":[220],"comprehensive":[222],"comparisons":[223],"existing":[225,243],"(and":[226],"future)":[227],"And,":[232],"time,":[236],"sheds":[238],"light":[239],"well":[242],"settings,":[248],"establishing":[249],"promising":[251],"reference":[252],"baseline.":[254]},"counts_by_year":[],"updated_date":"2026-02-26T06:34:08.959763","created_date":"2026-02-26T00:00:00"}