{"id":"https://openalex.org/W3205325461","doi":"https://doi.org/10.1145/3474085.3475228","title":"AKECP: Adaptive Knowledge Extraction from Feature Maps for Fast and Efficient Channel Pruning","display_name":"AKECP: Adaptive Knowledge Extraction from Feature Maps for Fast and Efficient Channel Pruning","publication_year":2021,"publication_date":"2021-10-17","ids":{"openalex":"https://openalex.org/W3205325461","doi":"https://doi.org/10.1145/3474085.3475228","mag":"3205325461"},"language":"en","primary_location":{"id":"doi:10.1145/3474085.3475228","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3474085.3475228","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100705310","display_name":"Haonan Zhang","orcid":"https://orcid.org/0000-0002-4239-6141"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haonan Zhang","raw_affiliation_strings":["Xi'an Jiaotong University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080046377","display_name":"Longjun Liu","orcid":"https://orcid.org/0000-0002-7467-4994"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Longjun Liu","raw_affiliation_strings":["Xi'an Jiaotong University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060272424","display_name":"Hengyi Zhou","orcid":"https://orcid.org/0000-0002-8433-4213"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hengyi Zhou","raw_affiliation_strings":["Xi'an Jiaotong University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025614581","display_name":"Wenxuan Hou","orcid":null},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenxuan Hou","raw_affiliation_strings":["Xi'an Jiaotong University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100706029","display_name":"Hongbin Sun","orcid":"https://orcid.org/0000-0003-2153-2906"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongbin Sun","raw_affiliation_strings":["Xi'an Jiaotong University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047405956","display_name":"Nanning Zheng","orcid":"https://orcid.org/0000-0003-1608-8257"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nanning Zheng","raw_affiliation_strings":["Xi'an Jiaotong University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100705310"],"corresponding_institution_ids":["https://openalex.org/I87445476"],"apc_list":null,"apc_paid":null,"fwci":1.7483,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.8694162,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"648","last_page":"657"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9969000220298767,"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":0.9833999872207642,"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/flops","display_name":"FLOPS","score":0.8712722063064575},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.7903995513916016},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7874701023101807},{"id":"https://openalex.org/keywords/memory-footprint","display_name":"Memory footprint","score":0.5948703289031982},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.5898511409759521},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5785735249519348},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5312076807022095},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.521639883518219},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5081350803375244},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4758572280406952},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.47470465302467346},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4497586786746979},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4497237503528595},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3404494524002075},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14151737093925476},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.13637739419937134}],"concepts":[{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.8712722063064575},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.7903995513916016},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7874701023101807},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.5948703289031982},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.5898511409759521},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5785735249519348},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5312076807022095},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.521639883518219},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5081350803375244},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4758572280406952},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.47470465302467346},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4497586786746979},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4497237503528595},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3404494524002075},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14151737093925476},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.13637739419937134},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3474085.3475228","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3474085.3475228","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8553522479","display_name":null,"funder_award_id":"61774125, 61790563","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W2112796928","https://openalex.org/W2117539524","https://openalex.org/W2194775991","https://openalex.org/W2295107390","https://openalex.org/W2300242332","https://openalex.org/W2737121650","https://openalex.org/W2748428003","https://openalex.org/W2754084392","https://openalex.org/W2795077673","https://openalex.org/W2799197246","https://openalex.org/W2883780447","https://openalex.org/W2924515500","https://openalex.org/W2924888702","https://openalex.org/W2928560789","https://openalex.org/W2951868790","https://openalex.org/W2951978180","https://openalex.org/W2952432176","https://openalex.org/W2962697884","https://openalex.org/W2962851801","https://openalex.org/W2963145730","https://openalex.org/W2963363373","https://openalex.org/W2963382930","https://openalex.org/W2963446712","https://openalex.org/W2964233199","https://openalex.org/W2964266063","https://openalex.org/W2966256598","https://openalex.org/W2981954115","https://openalex.org/W2984618279","https://openalex.org/W2997336365","https://openalex.org/W3010821730","https://openalex.org/W3011357794","https://openalex.org/W3022018744","https://openalex.org/W3034288893","https://openalex.org/W3034513523","https://openalex.org/W3034674511","https://openalex.org/W3034818206","https://openalex.org/W3035377608","https://openalex.org/W3041028268","https://openalex.org/W3048387920","https://openalex.org/W3049293589","https://openalex.org/W3092718160","https://openalex.org/W3092779813","https://openalex.org/W3092900809","https://openalex.org/W3093038900","https://openalex.org/W3093094504","https://openalex.org/W3093105758","https://openalex.org/W3093220398","https://openalex.org/W3093246726","https://openalex.org/W3093412994","https://openalex.org/W3093427603","https://openalex.org/W3106029750","https://openalex.org/W3107407793","https://openalex.org/W3118608800","https://openalex.org/W4234552385"],"related_works":["https://openalex.org/W4315697128","https://openalex.org/W3102845713","https://openalex.org/W2971502891","https://openalex.org/W3205506801","https://openalex.org/W4280599700","https://openalex.org/W3183570023","https://openalex.org/W4382323155","https://openalex.org/W2016508734","https://openalex.org/W4287067436","https://openalex.org/W2986126107"],"abstract_inverted_index":{"Pruning":[0,93],"can":[1,96,144,165],"remove":[2],"redundant":[3],"parameters":[4,191],"and":[5,16,73,101,112,146,176,192,211],"structures":[6],"of":[7,23,41,71,81,110,126,132,190,194,208,214,219],"Deep":[8],"Neural":[9],"Networks":[10],"(DNNs)":[11],"to":[12,32,61,78,128,138],"reduce":[13],"inference":[14],"time":[15],"memory":[17,209],"overhead.":[18],"As":[19],"an":[20,117],"important":[21],"component":[22],"neural":[24],"networks,":[25],"the":[26,39,52,79,98,108,123,130,139,150,180],"feature":[27],"map":[28],"(FM)":[29],"has":[30,178],"stated":[31],"be":[33],"adopted":[34],"for":[35,54,91,183],"network":[36,99,134],"pruning.":[37,55],"However,":[38],"majority":[40],"FM-based":[42],"pruning":[43,65,76],"methods":[44],"do":[45],"not":[46],"fully":[47],"investigate":[48,107],"effective":[49,114,124,140],"knowledge":[50,115,125,141],"in":[51],"FM":[53],"In":[56,83,103],"addition,":[57],"it":[58,177],"is":[59],"challenging":[60],"design":[62],"a":[63,68],"robust":[64],"criterion":[66],"with":[67,116,152,197,216],"small":[69],"number":[70],"images":[72],"achieve":[74],"parallel":[75],"due":[77],"variability":[80],"FMs.":[82],"this":[84],"paper,":[85],"we":[86,105,121],"propose":[87],"Adaptive":[88],"Knowledge":[89],"Extraction":[90],"Channel":[92],"(AKECP),":[94],"which":[95],"compress":[97,166],"fast":[100],"efficiently.":[102],"AKECP,":[104],"first":[106],"characteristics":[109],"FMs":[111,127],"extract":[113],"adaptive":[118],"scheme.":[119],"Secondly,":[120],"formulate":[122],"measure":[129],"importance":[131],"corresponding":[133],"channels.":[135],"Thirdly,":[136],"thanks":[137],"extraction,":[142],"AKECP":[143,187,205],"efficiently":[145],"simultaneously":[147],"prune":[148],"all":[149],"layers":[151],"extremely":[153],"few":[154],"or":[155],"even":[156],"one":[157],"image.":[158],"Experimental":[159],"results":[160],"show":[161],"that":[162],"our":[163],"method":[164],"various":[167],"networks":[168],"on":[169,185,203],"different":[170],"datasets":[171],"without":[172],"introducing":[173],"additional":[174],"constraints,":[175],"advanced":[179],"state-of-the-arts.":[181],"Notably,":[182],"ResNet-110":[184],"CIFAR-10,":[186],"achieves":[188],"59.9%":[189],"59.8%":[193],"FLOPs":[195,215],"reduction":[196],"negligible":[198],"accuracy":[199,221],"loss.":[200],"For":[201],"ResNet-50":[202],"ImageNet,":[204],"saves":[206],"40.5%":[207],"footprint":[210],"reduces":[212],"44.1%":[213],"only":[217],"0.32%":[218],"Top-1":[220],"drop.":[222]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":7}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}