{"id":"https://openalex.org/W4312493836","doi":"https://doi.org/10.1109/tcsvt.2022.3216101","title":"Filter Clustering for Compressing CNN Model With Better Feature Diversity","display_name":"Filter Clustering for Compressing CNN Model With Better Feature Diversity","publication_year":2022,"publication_date":"2022-11-03","ids":{"openalex":"https://openalex.org/W4312493836","doi":"https://doi.org/10.1109/tcsvt.2022.3216101"},"language":"en","primary_location":{"id":"doi:10.1109/tcsvt.2022.3216101","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2022.3216101","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Circuits and Systems for Video Technology","raw_type":"journal-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/A5100657484","display_name":"Zhenyu Wang","orcid":"https://orcid.org/0000-0002-4259-3073"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhenyu Wang","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi’an, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100458706","display_name":"Xuemei Xie","orcid":"https://orcid.org/0000-0001-7857-0845"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuemei Xie","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi’an, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101439992","display_name":"Qinghang Zhao","orcid":"https://orcid.org/0000-0003-0116-8975"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinghang Zhao","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi’an, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101549504","display_name":"Guangming Shi","orcid":"https://orcid.org/0000-0003-2179-3292"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangming Shi","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi’an, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100657484"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":1.2233,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.80722273,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"33","issue":"12","first_page":"7385","last_page":"7397"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9990000128746033,"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":0.9990000128746033,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9947999715805054,"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/T12676","display_name":"Machine Learning and ELM","score":0.991100013256073,"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/computer-science","display_name":"Computer science","score":0.7192750573158264},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6746532320976257},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.6125559210777283},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5748290419578552},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5683849453926086},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5280219912528992},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4908319115638733},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4455040991306305},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3372640609741211},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.10501575469970703}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7192750573158264},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6746532320976257},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6125559210777283},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5748290419578552},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5683849453926086},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5280219912528992},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4908319115638733},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4455040991306305},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3372640609741211},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.10501575469970703},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcsvt.2022.3216101","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2022.3216101","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Circuits and Systems for Video Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3049013466","display_name":null,"funder_award_id":"PCL2021A12","funder_id":"https://openalex.org/F4320318558","funder_display_name":"Peng Cheng Laboratory"},{"id":"https://openalex.org/G4150153631","display_name":null,"funder_award_id":"61836008","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7040859413","display_name":null,"funder_award_id":"62104182","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320318558","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":83,"referenced_works":["https://openalex.org/W1821462560","https://openalex.org/W1971621572","https://openalex.org/W1998871699","https://openalex.org/W2117539524","https://openalex.org/W2194775991","https://openalex.org/W2276892413","https://openalex.org/W2469490737","https://openalex.org/W2495425901","https://openalex.org/W2736953746","https://openalex.org/W2754084392","https://openalex.org/W2798170643","https://openalex.org/W2798275680","https://openalex.org/W2805933112","https://openalex.org/W2808168148","https://openalex.org/W2883780447","https://openalex.org/W2887287293","https://openalex.org/W2890788829","https://openalex.org/W2919115771","https://openalex.org/W2924515500","https://openalex.org/W2928560789","https://openalex.org/W2962851801","https://openalex.org/W2963163009","https://openalex.org/W2963363373","https://openalex.org/W2963382930","https://openalex.org/W2964217848","https://openalex.org/W2964228333","https://openalex.org/W2964233199","https://openalex.org/W2965862774","https://openalex.org/W3028151507","https://openalex.org/W3034251466","https://openalex.org/W3034513523","https://openalex.org/W3034652926","https://openalex.org/W3034797615","https://openalex.org/W3035086596","https://openalex.org/W3035414587","https://openalex.org/W3039041203","https://openalex.org/W3104263540","https://openalex.org/W3107073258","https://openalex.org/W3116646121","https://openalex.org/W3118608800","https://openalex.org/W3126817395","https://openalex.org/W3155573568","https://openalex.org/W3167976421","https://openalex.org/W3181161645","https://openalex.org/W3211878177","https://openalex.org/W4205252812","https://openalex.org/W4213004369","https://openalex.org/W4214929894","https://openalex.org/W4220897963","https://openalex.org/W4221157729","https://openalex.org/W4226191776","https://openalex.org/W4226329588","https://openalex.org/W4282973707","https://openalex.org/W4297775537","https://openalex.org/W6604254268","https://openalex.org/W6637373629","https://openalex.org/W6638523607","https://openalex.org/W6677103964","https://openalex.org/W6677580257","https://openalex.org/W6684563725","https://openalex.org/W6685405536","https://openalex.org/W6720242923","https://openalex.org/W6723181079","https://openalex.org/W6726275242","https://openalex.org/W6729956949","https://openalex.org/W6734062232","https://openalex.org/W6737664043","https://openalex.org/W6751876697","https://openalex.org/W6751979845","https://openalex.org/W6755034786","https://openalex.org/W6755166560","https://openalex.org/W6755843862","https://openalex.org/W6756040250","https://openalex.org/W6762450006","https://openalex.org/W6762610423","https://openalex.org/W6762718338","https://openalex.org/W6785994361","https://openalex.org/W6787972765","https://openalex.org/W6789946416","https://openalex.org/W6810495020","https://openalex.org/W6839051737","https://openalex.org/W6842607031","https://openalex.org/W7064683377"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W2373300491","https://openalex.org/W1212596013","https://openalex.org/W2378744544","https://openalex.org/W2594301978","https://openalex.org/W2379704676","https://openalex.org/W4206442282","https://openalex.org/W2384505857","https://openalex.org/W2355171581","https://openalex.org/W4229439743"],"abstract_inverted_index":{"As":[0],"a":[1,135,177,192,229,237,264],"practical":[2],"approach":[3,195],"for":[4],"compressing":[5],"convolutional":[6],"neural":[7],"networks":[8],"(CNNs),":[9],"network":[10],"pruning":[11,53,153],"has":[12],"been":[13],"rapidly":[14],"developed":[15],"in":[16,48,63,77,104,108,121,151],"recent":[17],"years.":[18],"The":[19,51,68,91],"conventional":[20],"methods":[21,45,296],"prune":[22],"inactive":[23,189],"filters":[24,182],"permanently":[25],"from":[26,159,212,253],"models":[27,76,283],"to":[28,61,128,143,163,198,217,248,268],"reduce":[29,117,289],"the":[30,38,56,73,78,84,100,109,118,122,145,165,173,200,203,210,219,250,257,277,294],"width":[31],"of":[32,65,75,86,147,167,202,221],"each":[33],"layer":[34],"and":[35,112,125,154,180,232,262,288],"then":[36],"train":[37],"pruned":[39,123],"model":[40,124,130,157,174],"until":[41],"convergence.":[42],"However,":[43],"such":[44,271],"have":[46],"limitations":[47,116],"that:":[49],"(1)":[50],"activation-based":[52],"criteria":[54],"ignore":[55],"correlation":[57],"between":[58],"filters,":[59],"leading":[60],"attenuation":[62],"types":[64],"features;":[66,89],"(2)":[67],"permanent":[69],"filter":[70,139,206],"removal":[71],"restricts":[72],"architecture":[74],"subsequent":[79],"training":[80,231],"so":[81],"that":[82,98,276],"reducing":[83],"chances":[85],"learning":[87,222],"more":[88,223,291],"(3)":[90],"single-width":[92],"compression":[93,136,175],"may":[94],"generate":[95],"narrow":[96,258],"layers":[97,111],"block":[99],"information":[101],"flow,":[102],"resulting":[103],"limited":[105],"feature":[106,119,149,251,286],"capacity":[107,252],"next":[110],"hard":[113],"optimization.":[114],"These":[115],"diversity":[120,150,287],"thus":[126],"lead":[127],"sub-optimal":[129],"quality.":[131],"In":[132],"this":[133],"paper,":[134],"method":[137,279],"named":[138],"clustering":[140,178,233],"is":[141,196],"proposed":[142,278],"rectify":[144],"problem":[146],"poor":[148],"traditional":[152],"achieve":[155,281],"better":[156,285],"quality":[158],"three":[160],"perspectives.":[161],"Firstly,":[162],"maintain":[164],"variety":[166],"features":[168,224],"after":[169],"pruning,":[170],"we":[171,227,260],"treat":[172],"as":[176],"task":[179],"merge":[181],"with":[183,284],"similar":[184],"outputs,":[185],"rather":[186],"than":[187,293],"removing":[188],"filters.":[190],"Specifically,":[191],"handy":[193],"estimation":[194],"designed":[197],"convert":[199],"similarity":[201],"output":[204],"into":[205],"similarity,":[207],"which":[208,235],"liberates":[209],"measurement":[211],"sampling":[213],"numerous":[214],"images.":[215],"Secondly,":[216],"increase":[218],"probability":[220],"during":[225],"training,":[226],"propose":[228],"periodic":[230],"pipeline,":[234],"creates":[236],"larger":[238],"optimization":[239],"space":[240],"by":[241,256],"dynamically":[242],"exploring":[243],"different":[244],"sub-model":[245],"architectures.":[246],"Finally,":[247],"prevent":[249],"being":[254],"influenced":[255],"layers,":[259],"introduce":[261],"leverage":[263],"fusible":[265],"anti-blocking":[266],"branch":[267],"smoothly":[269],"remove":[270],"layers.":[272],"Extensive":[273],"experiments":[274],"demonstrate":[275],"can":[280],"compact":[282],"1%~15%":[290],"calculations":[292],"previous":[295],"while":[297],"maintaining":[298],"performance.":[299]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-12T08:34:05.389933","created_date":"2025-10-10T00:00:00"}