{"id":"https://openalex.org/W3181161645","doi":"https://doi.org/10.1109/cvpr46437.2021.01467","title":"Convolutional Neural Network Pruning with Structural Redundancy Reduction","display_name":"Convolutional Neural Network Pruning with Structural Redundancy Reduction","publication_year":2021,"publication_date":"2021-06-01","ids":{"openalex":"https://openalex.org/W3181161645","doi":"https://doi.org/10.1109/cvpr46437.2021.01467","mag":"3181161645"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr46437.2021.01467","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr46437.2021.01467","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","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/A5078821760","display_name":"Zi Wang","orcid":"https://orcid.org/0000-0001-8635-8334"},"institutions":[{"id":"https://openalex.org/I75027704","display_name":"University of Tennessee at Knoxville","ror":"https://ror.org/020f3ap87","country_code":"US","type":"education","lineage":["https://openalex.org/I75027704"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zi Wang","raw_affiliation_strings":["The University of Tennessee, Knoxville, TN, USA"],"affiliations":[{"raw_affiliation_string":"The University of Tennessee, Knoxville, TN, USA","institution_ids":["https://openalex.org/I75027704"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100324621","display_name":"Chengcheng Li","orcid":"https://orcid.org/0000-0003-2945-0462"},"institutions":[{"id":"https://openalex.org/I75027704","display_name":"University of Tennessee at Knoxville","ror":"https://ror.org/020f3ap87","country_code":"US","type":"education","lineage":["https://openalex.org/I75027704"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chengcheng Li","raw_affiliation_strings":["The University of Tennessee, Knoxville, TN, USA"],"affiliations":[{"raw_affiliation_string":"The University of Tennessee, Knoxville, TN, USA","institution_ids":["https://openalex.org/I75027704"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100755826","display_name":"Xiangyang Wang","orcid":"https://orcid.org/0000-0003-2276-539X"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangyang Wang","raw_affiliation_strings":["Sun Yat-sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5078821760"],"corresponding_institution_ids":["https://openalex.org/I75027704"],"apc_list":null,"apc_paid":null,"fwci":15.5992,"has_fulltext":false,"cited_by_count":197,"citation_normalized_percentile":{"value":0.99408447,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"14908","last_page":"14917"},"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9994000196456909,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9991999864578247,"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/redundancy","display_name":"Redundancy (engineering)","score":0.8918310403823853},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7669376134872437},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7366665601730347},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.6564326882362366},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5845488905906677},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5093911290168762},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.44005680084228516},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4241727590560913},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3221732974052429},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12161886692047119}],"concepts":[{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.8918310403823853},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7669376134872437},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7366665601730347},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6564326882362366},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5845488905906677},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5093911290168762},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.44005680084228516},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4241727590560913},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3221732974052429},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12161886692047119},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvpr46437.2021.01467","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr46437.2021.01467","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.5799999833106995,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":88,"referenced_works":["https://openalex.org/W192134417","https://openalex.org/W1686810756","https://openalex.org/W1821462560","https://openalex.org/W1885185971","https://openalex.org/W2099471712","https://openalex.org/W2108598243","https://openalex.org/W2114766824","https://openalex.org/W2119144962","https://openalex.org/W2120972216","https://openalex.org/W2123469553","https://openalex.org/W2145339207","https://openalex.org/W2163605009","https://openalex.org/W2172166488","https://openalex.org/W2194775991","https://openalex.org/W2257979135","https://openalex.org/W2285660444","https://openalex.org/W2495425901","https://openalex.org/W2707890836","https://openalex.org/W2754084392","https://openalex.org/W2785856116","https://openalex.org/W2798170643","https://openalex.org/W2803543130","https://openalex.org/W2808168148","https://openalex.org/W2883780447","https://openalex.org/W2890788829","https://openalex.org/W2896409484","https://openalex.org/W2905741102","https://openalex.org/W2919115771","https://openalex.org/W2924515500","https://openalex.org/W2928560789","https://openalex.org/W2944355599","https://openalex.org/W2945176031","https://openalex.org/W2945403477","https://openalex.org/W2945908221","https://openalex.org/W2947996606","https://openalex.org/W2950656546","https://openalex.org/W2951569836","https://openalex.org/W2952164265","https://openalex.org/W2954650510","https://openalex.org/W2962965870","https://openalex.org/W2963092440","https://openalex.org/W2963094099","https://openalex.org/W2963145730","https://openalex.org/W2963163009","https://openalex.org/W2963416938","https://openalex.org/W2963446712","https://openalex.org/W2963674932","https://openalex.org/W2964019666","https://openalex.org/W2964233199","https://openalex.org/W2964299589","https://openalex.org/W2969958526","https://openalex.org/W2970790493","https://openalex.org/W2970971581","https://openalex.org/W2990525501","https://openalex.org/W3028304412","https://openalex.org/W3034251466","https://openalex.org/W3034513523","https://openalex.org/W3034693242","https://openalex.org/W3041028268","https://openalex.org/W3118608800","https://openalex.org/W3182242813","https://openalex.org/W4295312788","https://openalex.org/W4320013936","https://openalex.org/W6637373629","https://openalex.org/W6638523607","https://openalex.org/W6677103964","https://openalex.org/W6677580257","https://openalex.org/W6678000929","https://openalex.org/W6685405536","https://openalex.org/W6723181079","https://openalex.org/W6725739302","https://openalex.org/W6726275242","https://openalex.org/W6739917289","https://openalex.org/W6747766405","https://openalex.org/W6750209611","https://openalex.org/W6751551026","https://openalex.org/W6753767121","https://openalex.org/W6755034786","https://openalex.org/W6755843862","https://openalex.org/W6757036269","https://openalex.org/W6762450006","https://openalex.org/W6762610423","https://openalex.org/W6763067651","https://openalex.org/W6763207085","https://openalex.org/W6766978945","https://openalex.org/W6770508256","https://openalex.org/W6779249178","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W4246352526","https://openalex.org/W2121910908","https://openalex.org/W915438175","https://openalex.org/W4230315250"],"abstract_inverted_index":{"Convolutional":[0],"neural":[1],"network":[2,12,21,33,64,101,126],"(CNN)":[3],"pruning":[4,22,65,75,85,102],"has":[5],"become":[6],"one":[7],"of":[8,108],"the":[9,27,32,63,77,80,86,115,119,137],"most":[10,81,120],"successful":[11],"compression":[13],"approaches":[14],"in":[15,31,67,76,114],"recent":[16],"years.":[17],"Existing":[18],"works":[19],"on":[20,25,94,123],"usually":[23],"focus":[24],"removing":[26],"least":[28,87],"important":[29,88],"filters":[30,89,113],"to":[34],"achieve":[35],"compact":[36],"architectures.":[37],"In":[38],"this":[39,95],"study,":[40],"we":[41,97],"claim":[42],"that":[43,74,104,131],"identifying":[44],"structural":[45,82,106],"redundancy":[46,69,83,107],"plays":[47],"a":[48,68,100,109],"more":[49],"essential":[50],"role":[51],"than":[52],"finding":[53],"unimportant":[54],"filters,":[55],"theoretically":[56],"and":[57,72,111,128],"empirically.":[58],"We":[59],"first":[60],"statistically":[61],"model":[62],"problem":[66],"reduction":[70],"perspective":[71],"find":[73],"layer(s)":[78,117],"with":[79,118],"outperforms":[84,136],"across":[90],"all":[91],"layers.":[92],"Based":[93],"finding,":[96],"then":[98],"propose":[99],"approach":[103,134],"identifies":[105],"CNN":[110],"prunes":[112],"selected":[116],"redundancy.":[121],"Experiments":[122],"various":[124],"benchmark":[125],"architectures":[127],"datasets":[129],"show":[130],"our":[132],"proposed":[133],"significantly":[135],"previous":[138],"state-of-the-art.":[139]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":29},{"year":2024,"cited_by_count":47},{"year":2023,"cited_by_count":51},{"year":2022,"cited_by_count":49},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2025-10-10T00:00:00"}