{"id":"https://openalex.org/W4385764558","doi":"https://doi.org/10.24963/ijcai.2023/153","title":"Data Level Lottery Ticket Hypothesis for Vision Transformers","display_name":"Data Level Lottery Ticket Hypothesis for Vision Transformers","publication_year":2023,"publication_date":"2023-08-01","ids":{"openalex":"https://openalex.org/W4385764558","doi":"https://doi.org/10.24963/ijcai.2023/153"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2023/153","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2023/153","pdf_url":"https://www.ijcai.org/proceedings/2023/0153.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2023/0153.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101655527","display_name":"Xuan Shen","orcid":"https://orcid.org/0000-0003-4965-7321"},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Xuan Shen","raw_affiliation_strings":["Northeastern University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078971265","display_name":"Zhenglun Kong","orcid":"https://orcid.org/0000-0002-8120-4456"},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Zhenglun Kong","raw_affiliation_strings":["Northeastern University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084528785","display_name":"Minghai Qin","orcid":"https://orcid.org/0000-0001-5172-5309"},"institutions":[{"id":"https://openalex.org/I118271192","display_name":"Western Digital (Japan)","ror":"https://ror.org/0521k5n17","country_code":"JP","type":"company","lineage":["https://openalex.org/I118271192","https://openalex.org/I4210121352"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Minghai Qin","raw_affiliation_strings":["Western Digital Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Western Digital Research","institution_ids":["https://openalex.org/I118271192"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089349472","display_name":"Peiyan Dong","orcid":"https://orcid.org/0000-0001-5287-5149"},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Peiyan Dong","raw_affiliation_strings":["Northeastern University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100957385","display_name":"Geng Yuan","orcid":null},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Geng Yuan","raw_affiliation_strings":["Northeastern University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101543801","display_name":"Xin Meng","orcid":"https://orcid.org/0000-0003-1376-0917"},"institutions":[{"id":"https://openalex.org/I111483173","display_name":"King University","ror":"https://ror.org/01evb6z23","country_code":"US","type":"education","lineage":["https://openalex.org/I111483173"]},{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Xin Meng","raw_affiliation_strings":["Peking university","Peking University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking university","institution_ids":["https://openalex.org/I111483173"]},{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050748634","display_name":"Hao Tang","orcid":"https://orcid.org/0000-0002-2077-1246"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Hao Tang","raw_affiliation_strings":["ETH Zurich"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ETH Zurich","institution_ids":["https://openalex.org/I35440088"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074448953","display_name":"Xiaolong Ma","orcid":"https://orcid.org/0000-0003-1392-2787"},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaolong Ma","raw_affiliation_strings":["Clemson University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Clemson University","institution_ids":["https://openalex.org/I8078737"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100651384","display_name":"Yanzhi Wang","orcid":"https://orcid.org/0000-0002-3024-7990"},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Yanzhi Wang","raw_affiliation_strings":["Northeastern University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]}]}],"institutions":[],"countries_distinct_count":5,"institutions_distinct_count":9,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5614,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.67823685,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1378","last_page":"1386"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9984999895095825,"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":0.9984999895095825,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/ticket","display_name":"Ticket","score":0.8339853286743164},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7089333534240723},{"id":"https://openalex.org/keywords/lottery","display_name":"Lottery","score":0.594490110874176},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.5393012166023254},{"id":"https://openalex.org/keywords/scratch","display_name":"Scratch","score":0.4847564399242401},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4574103355407715},{"id":"https://openalex.org/keywords/subnetwork","display_name":"Subnetwork","score":0.44923242926597595},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44229984283447266},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4233863353729248},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3987765610218048},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3974246680736542},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34415021538734436},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.1856972873210907},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17074429988861084},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09246721863746643}],"concepts":[{"id":"https://openalex.org/C2776540713","wikidata":"https://www.wikidata.org/wiki/Q7800647","display_name":"Ticket","level":2,"score":0.8339853286743164},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7089333534240723},{"id":"https://openalex.org/C2777340749","wikidata":"https://www.wikidata.org/wiki/Q6684955","display_name":"Lottery","level":2,"score":0.594490110874176},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.5393012166023254},{"id":"https://openalex.org/C2781235140","wikidata":"https://www.wikidata.org/wiki/Q275131","display_name":"Scratch","level":2,"score":0.4847564399242401},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4574103355407715},{"id":"https://openalex.org/C2780186347","wikidata":"https://www.wikidata.org/wiki/Q11414","display_name":"Subnetwork","level":2,"score":0.44923242926597595},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44229984283447266},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4233863353729248},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3987765610218048},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3974246680736542},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34415021538734436},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.1856972873210907},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17074429988861084},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09246721863746643},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2023/153","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2023/153","pdf_url":"https://www.ijcai.org/proceedings/2023/0153.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2023/153","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2023/153","pdf_url":"https://www.ijcai.org/proceedings/2023/0153.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385764558.pdf"},"referenced_works_count":42,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2764043458","https://openalex.org/W2791091755","https://openalex.org/W2805003733","https://openalex.org/W2915589364","https://openalex.org/W2948130861","https://openalex.org/W2970277060","https://openalex.org/W2984218712","https://openalex.org/W2995816250","https://openalex.org/W2996074092","https://openalex.org/W3094502228","https://openalex.org/W3096609285","https://openalex.org/W3103754749","https://openalex.org/W3104263050","https://openalex.org/W3118608800","https://openalex.org/W3138516171","https://openalex.org/W3168124404","https://openalex.org/W3169769781","https://openalex.org/W3170863103","https://openalex.org/W3170874841","https://openalex.org/W3171206729","https://openalex.org/W3172801447","https://openalex.org/W3174402370","https://openalex.org/W3176010265","https://openalex.org/W3177143648","https://openalex.org/W3203701986","https://openalex.org/W3204801262","https://openalex.org/W3213530295","https://openalex.org/W4214633470","https://openalex.org/W4221156361","https://openalex.org/W4225930227","https://openalex.org/W4226052632","https://openalex.org/W4226066935","https://openalex.org/W4226484461","https://openalex.org/W4226517473","https://openalex.org/W4239072543","https://openalex.org/W4287116734","https://openalex.org/W4308536459","https://openalex.org/W4312340826","https://openalex.org/W4312910119","https://openalex.org/W4313166619","https://openalex.org/W4382466174"],"related_works":["https://openalex.org/W2060724872","https://openalex.org/W2004731113","https://openalex.org/W2082094785","https://openalex.org/W2202198356","https://openalex.org/W3087203342","https://openalex.org/W2377184161","https://openalex.org/W228984114","https://openalex.org/W4226360758","https://openalex.org/W1556639976","https://openalex.org/W1899764947"],"abstract_inverted_index":{"The":[0,187,235],"conventional":[1,64,225],"lottery":[2],"ticket":[3,66,163],"hypothesis":[4],"(LTH)":[5],"claims":[6],"that":[7,29,62,111,190],"there":[8,102,191],"exists":[9,103],"a":[10,14,19,104,112,151,162,193],"sparse":[11],"subnetwork":[12],"within":[13],"dense":[15,42],"neural":[16],"network":[17],"and":[18,126,184,205,223],"proper":[20],"random":[21],"initialization":[22],"method,":[23],"called":[24],"the":[25,41,45,63,82,95,131,146,157,167,172,197,216,224,230],"winning":[26,65,147,168,203],"ticket,":[27],"such":[28,110],"it":[30],"can":[31,114],"be":[32,115],"trained":[33,116,133,201],"from":[34,117],"scratch":[35,118],"to":[36,69,86,130,165],"almost":[37],"as":[38,40],"good":[39],"counterpart.":[43],"Meanwhile,":[44],"research":[46],"of":[47,74,90,98,106,124,143,154,174,179,199,232],"LTH":[48,83,226],"in":[49,156],"vision":[50],"transformers":[51],"(ViTs)":[52],"is":[53,67,192],"scarcely":[54],"evaluated.":[55],"In":[56],"this":[57,122,141],"paper,":[58],"we":[59,80],"first":[60],"show":[61,189],"hard":[68],"find":[70],"at":[71,240],"weight":[72],"level":[73],"ViTs":[75,85,132],"by":[76,94,119,134],"existing":[77],"methods.":[78],"Then,":[79],"generalize":[81],"for":[84,176,227],"input":[87,96,107,144,158],"data":[88],"consisting":[89],"image":[91,108,137],"patches":[92,109,125,145,175],"inspired":[93],"dependence":[97],"ViTs.":[99],"That":[100],"is,":[101],"subset":[105,123,142],"ViT":[113],"using":[120,135],"only":[121],"achieve":[127],"similar":[128],"accuracy":[129],"all":[136],"patches.":[138],"We":[139,160,214],"call":[140],"tickets,":[148],"which":[149,209],"represent":[150],"significant":[152],"amount":[153],"information":[155],"data.":[159],"use":[161],"selector":[164],"generate":[166],"tickets":[169,204],"based":[170],"on":[171],"informativeness":[173],"various":[177],"types":[178],"ViT,":[180],"including":[181],"DeiT,":[182],"LV-ViT,":[183],"Swin":[185],"Transformers.":[186],"experiments":[188],"clear":[194],"difference":[195],"between":[196,219],"performance":[198],"models":[200],"with":[202],"randomly":[206],"selected":[207],"subsets,":[208],"verifies":[210],"our":[211,220,233],"proposed":[212,221],"theory.":[213,234],"elaborate":[215],"analogical":[217],"similarity":[218],"Data-LTH-ViTs":[222],"further":[228],"verifying":[229],"integrity":[231],"Source":[236],"codes":[237],"are":[238],"available":[239],"https://github.com/shawnricecake/vit-lottery-ticket-input.":[241]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}