{"id":"https://openalex.org/W3201292656","doi":"https://doi.org/10.1109/lsp.2021.3101670","title":"A Dual Rank-Constrained Filter Pruning Approach for Convolutional Neural Networks","display_name":"A Dual Rank-Constrained Filter Pruning Approach for Convolutional Neural Networks","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3201292656","doi":"https://doi.org/10.1109/lsp.2021.3101670","mag":"3201292656"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2021.3101670","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2021.3101670","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","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/A5026237787","display_name":"Fugui Fan","orcid":"https://orcid.org/0000-0002-7429-0245"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fugui Fan","raw_affiliation_strings":["School of Electrical and Information Engineering, Tianjin University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-7429-0245","affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033713097","display_name":"Yuting Su","orcid":"https://orcid.org/0000-0001-5165-204X"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuting Su","raw_affiliation_strings":["School of Electrical and Information Engineering, Tianjin University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0001-5165-204X","affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000190108","display_name":"Peiguang Jing","orcid":"https://orcid.org/0000-0003-2648-7358"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peiguang Jing","raw_affiliation_strings":["School of Electrical and Information Engineering, Tianjin University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0003-2648-7358","affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100627862","display_name":"Wei L\u00fc","orcid":"https://orcid.org/0000-0002-6566-775X"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Lu","raw_affiliation_strings":["School of Electrical and Information Engineering, Tianjin University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-6566-775X","affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I162868743"],"apc_list":null,"apc_paid":null,"fwci":1.1644,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.80856553,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"28","issue":null,"first_page":"1734","last_page":"1738"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9979000091552734,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9979000091552734,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9952999949455261,"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":0.993399977684021,"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":0.6659339666366577},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.590890109539032},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5885686278343201},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5706878900527954},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.5455517172813416},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5413077473640442},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4979712963104248},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.42665353417396545},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.14860957860946655},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1413552463054657}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6659339666366577},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.590890109539032},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5885686278343201},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5706878900527954},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.5455517172813416},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5413077473640442},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4979712963104248},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.42665353417396545},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.14860957860946655},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1413552463054657},{"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":1,"locations":[{"id":"doi:10.1109/lsp.2021.3101670","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2021.3101670","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1617332088","https://openalex.org/W1686810756","https://openalex.org/W1804110266","https://openalex.org/W2033029203","https://openalex.org/W2052949247","https://openalex.org/W2097117768","https://openalex.org/W2100549954","https://openalex.org/W2121947440","https://openalex.org/W2142040002","https://openalex.org/W2153112160","https://openalex.org/W2167277498","https://openalex.org/W2194775991","https://openalex.org/W2253163421","https://openalex.org/W2341528187","https://openalex.org/W2509065397","https://openalex.org/W2899771611","https://openalex.org/W2924515500","https://openalex.org/W2948783659","https://openalex.org/W2951085447","https://openalex.org/W2962835968","https://openalex.org/W2962851801","https://openalex.org/W2962965870","https://openalex.org/W2963363373","https://openalex.org/W2964233199","https://openalex.org/W2970869172","https://openalex.org/W3007910141","https://openalex.org/W3034513523","https://openalex.org/W3098357269","https://openalex.org/W3101998545","https://openalex.org/W3118608800","https://openalex.org/W4205293427","https://openalex.org/W6637373629","https://openalex.org/W6675164516","https://openalex.org/W6691615350","https://openalex.org/W6726275242","https://openalex.org/W6756040250","https://openalex.org/W6787972765"],"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":{"Filter":[0],"pruning":[1,49],"has":[2],"attracted":[3],"increasing":[4],"attentions":[5],"to":[6,81,121],"compress":[7],"and":[8,60,95,156],"accelerate":[9],"the":[10,27,35,57,74,78,83,90,101,116,123,129,138,146],"convolutional":[11,52],"neural":[12,53],"networks":[13],"(CNNs)":[14],"on":[15,24,108,133],"computationally":[16],"restricted":[17],"devices.":[18],"Existing":[19],"related":[20],"methods":[21,148],"mainly":[22],"focus":[23],"independently":[25],"leveraging":[26],"spatial":[28],"information":[29],"of":[30,62,87,162],"individual":[31],"filters":[32,64,88,103],"while":[33],"ignoring":[34],"inner":[36],"correlation":[37],"among":[38],"filters.":[39],"In":[40],"this":[41],"letter,":[42],"we":[43],"propose":[44],"a":[45],"dual":[46],"rank-constrained":[47],"filter":[48,126],"approach":[50,76,140],"for":[51,89,111,127],"networks,":[54],"in":[55,151,154,158,160],"which":[56],"representation,":[58],"clustering,":[59],"identification":[61],"representative":[63],"are":[65,104],"integrated":[66],"into":[67],"an":[68],"adaptive":[69,91],"graph":[70,93],"regularization":[71],"framework.":[72],"Particularly,":[73],"proposed":[75,139],"utilizes":[77],"low-rank":[79],"constraint":[80,118],"capture":[82],"low-dimensional":[84],"intrinsic":[85],"representations":[86],"affinity":[92],"construction":[94],"clustering.":[96],"It":[97],"is":[98,119],"noteworthy":[99],"that":[100,137],"original":[102],"projected":[105],"as":[106],"points":[107],"Grassmann":[109],"manifold":[110],"geometrical":[112],"structure":[113],"preserving.":[114],"Meanwhile,":[115],"high-rank":[117],"employed":[120],"select":[122],"most":[124],"informative":[125],"representing":[128],"cluster.":[130],"Experimental":[131],"results":[132,143],"CIFAR-10":[134],"dataset":[135],"show":[136],"achieves":[141],"competitive":[142],"compared":[144],"with":[145],"state-of-the-art":[147],"by":[149],"87.1%":[150],"VGGNet-16,":[152],"52.4%":[153],"GoogLeNet":[155],"72.9%":[157],"ResNet-56":[159],"terms":[161],"model":[163],"parameters":[164],"compression.":[165]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}