{"id":"https://openalex.org/W4306808906","doi":"https://doi.org/10.48550/arxiv.2210.09223","title":"CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models","display_name":"CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4306808906","doi":"https://doi.org/10.48550/arxiv.2210.09223"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2210.09223","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_indexed_in_scopus":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":["Cornell University"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2210.09223","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5063706079","display_name":"Denis Kuznedelev","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kuznedelev, Denis","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074310887","display_name":"Eldar Kurtic","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kurtic, Eldar","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060660774","display_name":"Elias Frantar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Frantar, Elias","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5083822059","display_name":"Dan Alistarh","orcid":"https://orcid.org/0000-0003-3650-940X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alistarh, Dan","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"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":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":59},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9999,"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":0.9999,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.996,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9956,"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/pruning","display_name":"Pruning","score":0.71253425}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7688384},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.71253425},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.65847903},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.51133436},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.50678974},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46209684},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.32179293},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29552233},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2210.09223","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_indexed_in_scopus":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":["Cornell University"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2210.09223","pdf_url":"http://arxiv.org/pdf/2210.09223","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://api.datacite.org/dois/10.48550/arxiv.2210.09223","pdf_url":null,"source":{"id":"https://openalex.org/S4393179698","display_name":"DataCite API","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I4210145204","host_organization_name":"DataCite","host_organization_lineage":["https://openalex.org/I4210145204"],"host_organization_lineage_names":["DataCite"],"type":"metadata"},"license":null,"license_id":null,"version":null}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2210.09223","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_indexed_in_scopus":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":["Cornell University"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[{"score":0.49,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4235240664","https://openalex.org/W3214410901","https://openalex.org/W3204400881","https://openalex.org/W3204296682","https://openalex.org/W3183118997","https://openalex.org/W2965083567","https://openalex.org/W2917767146","https://openalex.org/W2389214306","https://openalex.org/W2373300491","https://openalex.org/W2295196644"],"abstract_inverted_index":{"Driven":[0],"by":[1,50],"significant":[2],"improvements":[3],"in":[4,17],"architectural":[5],"design":[6],"and":[7,90,97,124,161,163,181],"training":[8],"pipelines,":[9],"computer":[10],"vision":[11,115,195],"has":[12],"recently":[13],"experienced":[14],"dramatic":[15],"progress":[16],"terms":[18],"of":[19,169],"accuracy":[20,148,174,180,210],"on":[21,75,112,147],"classic":[22],"benchmarks":[23],"such":[24,43,117],"as":[25,34,44,118],"ImageNet.":[26],"These":[27],"highly-accurate":[28],"models":[29,116],"are":[30],"challenging":[31],"to":[32,38,137,166,171,186,205],"deploy,":[33],"they":[35],"appear":[36],"harder":[37],"compress":[39],"using":[40],"standard":[41],"techniques":[42],"pruning.":[45],"We":[46,105],"address":[47],"this":[48],"issue":[49],"introducing":[51],"the":[52,65,93,129,189],"Correlation":[53],"Aware":[54],"Pruner":[55],"(CAP),":[56],"a":[57,79],"new":[58,80],"unstructured":[59],"pruning":[60,94,160],"framework":[61],"which":[62,83],"significantly":[63],"pushes":[64],"compressibility":[66],"limits":[67],"for":[68,102,128,188],"state-of-the-art":[69],"architectures.":[70],"Our":[71,153],"method":[72],"is":[73,155],"based":[74],"two":[76],"technical":[77],"advancements:":[78],"theoretically-justified":[81],"pruner,":[82],"can":[84,134,164,201],"handle":[85],"complex":[86],"weight":[87],"correlations":[88],"accurately":[89],"efficiently":[91],"during":[92],"process":[95],"itself,":[96],"an":[98],"efficient":[99],"finetuning":[100],"procedure":[101],"post-compression":[103],"recovery.":[104],"validate":[106],"our":[107],"approach":[108,154],"via":[109,198],"extensive":[110],"experiments":[111],"several":[113],"modern":[114,122],"Vision":[119],"Transformers":[120],"(ViT),":[121],"CNNs,":[123],"ViT-CNN":[125],"hybrids,":[126],"showing":[127],"first":[130,190],"time":[131,191],"that":[132,192],"these":[133],"be":[135,203],"pruned":[136,204],"high":[138],"sparsity":[139],"levels":[140],"(e.g.":[141],"$\\geq":[142],"75$%)":[143],"with":[144,158,208],"low":[145],"impact":[146],"($\\leq":[149],"1$%":[150],"relative":[151],"drop).":[152],"also":[156,202],"compatible":[157],"structured":[159],"quantization,":[162],"lead":[165],"practical":[167],"speedups":[168],"1.5":[170],"2.4x":[172],"without":[173],"loss.":[175,211],"To":[176],"further":[177],"showcase":[178],"CAP's":[179],"scalability,":[182],"we":[183],"use":[184],"it":[185],"show":[187],"extremely-accurate":[193],"large":[194],"models,":[196],"trained":[197],"self-supervised":[199],"techniques,":[200],"moderate":[206],"sparsities,":[207],"negligible":[209]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4306808906","counts_by_year":[],"updated_date":"2025-03-02T09:11:42.479985","created_date":"2022-10-20"}