{"id":"https://openalex.org/W4285059684","doi":"https://doi.org/10.1109/tmm.2022.3189496","title":"FP-AGL: Filter Pruning With Adaptive Gradient Learning for Accelerating Deep Convolutional Neural Networks","display_name":"FP-AGL: Filter Pruning With Adaptive Gradient Learning for Accelerating Deep Convolutional Neural Networks","publication_year":2022,"publication_date":"2022-07-11","ids":{"openalex":"https://openalex.org/W4285059684","doi":"https://doi.org/10.1109/tmm.2022.3189496"},"language":"en","primary_location":{"id":"doi:10.1109/tmm.2022.3189496","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2022.3189496","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"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 Multimedia","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/A5110741547","display_name":"Nam Joon Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I118373667","display_name":"Seoul National University of Science and Technology","ror":"https://ror.org/00chfja07","country_code":"KR","type":"education","lineage":["https://openalex.org/I118373667"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Nam Joon Kim","raw_affiliation_strings":["Department of Electrical and Information Engineering and the Research Center for Electrical and Information Technology, Seoul National University of Science and Technology, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Information Engineering and the Research Center for Electrical and Information Technology, Seoul National University of Science and Technology, Seoul, Korea","institution_ids":["https://openalex.org/I118373667"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100371185","display_name":"Hyun Kim","orcid":"https://orcid.org/0000-0002-7962-657X"},"institutions":[{"id":"https://openalex.org/I118373667","display_name":"Seoul National University of Science and Technology","ror":"https://ror.org/00chfja07","country_code":"KR","type":"education","lineage":["https://openalex.org/I118373667"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyun Kim","raw_affiliation_strings":["Department of Electrical and Information Engineering and the Research Center for Electrical and Information Technology, Seoul National University of Science and Technology, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Information Engineering and the Research Center for Electrical and Information Technology, Seoul National University of Science and Technology, Seoul, Korea","institution_ids":["https://openalex.org/I118373667"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5110741547"],"corresponding_institution_ids":["https://openalex.org/I118373667"],"apc_list":null,"apc_paid":null,"fwci":4.8904,"has_fulltext":false,"cited_by_count":49,"citation_normalized_percentile":{"value":0.96379468,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"25","issue":null,"first_page":"5279","last_page":"5290"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9980999827384949,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9951000213623047,"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/computer-science","display_name":"Computer science","score":0.7898519039154053},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.6825991272926331},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6130123734474182},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5658088326454163},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5441348552703857},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.5361352562904358},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.522650957107544},{"id":"https://openalex.org/keywords/flops","display_name":"FLOPS","score":0.5125805735588074},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.4787449240684509},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47184184193611145},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4592743515968323},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.45908844470977783},{"id":"https://openalex.org/keywords/memory-footprint","display_name":"Memory footprint","score":0.45536038279533386},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.45352184772491455},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3702014088630676},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36411356925964355},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.09038463234901428}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7898519039154053},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6825991272926331},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6130123734474182},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5658088326454163},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5441348552703857},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.5361352562904358},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.522650957107544},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.5125805735588074},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.4787449240684509},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47184184193611145},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4592743515968323},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.45908844470977783},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.45536038279533386},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45352184772491455},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3702014088630676},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36411356925964355},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.09038463234901428},{"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/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","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/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmm.2022.3189496","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2022.3189496","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"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 Multimedia","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2281804537","display_name":null,"funder_award_id":"NRF-2019R1A6A1A03032119","funder_id":"https://openalex.org/F4320312071","funder_display_name":"Ministry of Education, Libya"}],"funders":[{"id":"https://openalex.org/F4320312071","display_name":"Ministry of Education, Libya","ror":"https://ror.org/02w030k33"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":87,"referenced_works":["https://openalex.org/W104184427","https://openalex.org/W1836465849","https://openalex.org/W1903029394","https://openalex.org/W2031489346","https://openalex.org/W2117539524","https://openalex.org/W2119144962","https://openalex.org/W2141155619","https://openalex.org/W2144794286","https://openalex.org/W2194775991","https://openalex.org/W2495425901","https://openalex.org/W2589646680","https://openalex.org/W2707890836","https://openalex.org/W2788715907","https://openalex.org/W2800371094","https://openalex.org/W2805003733","https://openalex.org/W2808168148","https://openalex.org/W2886851211","https://openalex.org/W2896006880","https://openalex.org/W2928560789","https://openalex.org/W2935524202","https://openalex.org/W2948253213","https://openalex.org/W2952164265","https://openalex.org/W2962965870","https://openalex.org/W2963125010","https://openalex.org/W2963150697","https://openalex.org/W2963163009","https://openalex.org/W2963446712","https://openalex.org/W2964001144","https://openalex.org/W2964266063","https://openalex.org/W2965862774","https://openalex.org/W2966256598","https://openalex.org/W2971275988","https://openalex.org/W2974893078","https://openalex.org/W2976540144","https://openalex.org/W2981609437","https://openalex.org/W2981958729","https://openalex.org/W2982605490","https://openalex.org/W2984618279","https://openalex.org/W2988803389","https://openalex.org/W2993182889","https://openalex.org/W2994749257","https://openalex.org/W3006772089","https://openalex.org/W3019048729","https://openalex.org/W3034251466","https://openalex.org/W3034408420","https://openalex.org/W3034513523","https://openalex.org/W3034818206","https://openalex.org/W3034971973","https://openalex.org/W3035297311","https://openalex.org/W3035414587","https://openalex.org/W3035467254","https://openalex.org/W3039489410","https://openalex.org/W3041028268","https://openalex.org/W3042011474","https://openalex.org/W3046669193","https://openalex.org/W3100837220","https://openalex.org/W3118608800","https://openalex.org/W3122390253","https://openalex.org/W3126405246","https://openalex.org/W3147182033","https://openalex.org/W3158487376","https://openalex.org/W4288257146","https://openalex.org/W4293584584","https://openalex.org/W4297775537","https://openalex.org/W6604254268","https://openalex.org/W6638667902","https://openalex.org/W6677580257","https://openalex.org/W6681015464","https://openalex.org/W6723181079","https://openalex.org/W6726275242","https://openalex.org/W6734169003","https://openalex.org/W6737664043","https://openalex.org/W6739917289","https://openalex.org/W6748035112","https://openalex.org/W6750227808","https://openalex.org/W6751979845","https://openalex.org/W6755526309","https://openalex.org/W6762450006","https://openalex.org/W6763591946","https://openalex.org/W6767064347","https://openalex.org/W6767327937","https://openalex.org/W6767534822","https://openalex.org/W6774108306","https://openalex.org/W6779249178","https://openalex.org/W6780627724","https://openalex.org/W6781550413","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W4206903459","https://openalex.org/W2754816816","https://openalex.org/W4366280654","https://openalex.org/W3160167280","https://openalex.org/W4231621013","https://openalex.org/W4362706668","https://openalex.org/W3008318776","https://openalex.org/W1977633006","https://openalex.org/W2041416246","https://openalex.org/W3020853991"],"abstract_inverted_index":{"Filter":[0],"pruning":[1,36,50],"is":[2],"a":[3,169,198,207],"technique":[4,130],"that":[5,77,95,131],"reduces":[6],"computational":[7,230],"complexity,":[8],"inference":[9],"time,":[10],"and":[11,153,184,229],"memory":[12],"footprint":[13],"by":[14,58,108,160],"removing":[15],"unnecessary":[16],"filters":[17,52,70,98],"in":[18,27,219,222],"convolutional":[19],"neural":[20],"networks":[21],"(CNNs)":[22],"with":[23,53,197],"an":[24,125],"acceptable":[25],"drop":[26,203],"accuracy,":[28],"consequently":[29],"accelerating":[30],"the":[31,47,59,69,75,83,89,106,110,116,119,137,157,163,175,178,189,211,225],"network.":[32],"Unlike":[33],"traditional":[34],"filter":[35],"methods":[37],"utilizing":[38],"zeroing-out":[39],"filters,":[40,164],"we":[41,104,123,173,209],"propose":[42,124],"two":[43],"techniques":[44],"to":[45,79,87,101,168,216],"achieve":[46,210],"effect":[48,91,111],"of":[49,92,112,177,224],"more":[51],"less":[54],"performance":[55,141,214],"degradation,":[56],"inspired":[57],"existing":[60],"research":[61],"on":[62,115,181,188,204],"centripetal":[63,93],"stochastic":[64],"gradient":[65,127],"descent":[66],"(C-SGD),":[67],"wherein":[68],"are":[71],"removed":[72,194],"only":[73],"when":[74],"ones":[76],"need":[78],"be":[80,144],"pruned":[81],"have":[82],"same":[84],"value.":[85],"First,":[86],"minimize":[88],"negative":[90],"vectors":[94,107],"gradually":[96],"make":[97],"come":[99],"closer":[100],"each":[102,113],"other,":[103],"redesign":[105],"considering":[109],"vector":[114],"loss-function":[117],"using":[118],"Taylor-based":[120],"method.":[121],"Second,":[122],"adaptive":[126],"learning":[128],"(AGL)":[129],"updates":[132],"weights":[133],"while":[134],"adaptively":[135],"changing":[136],"gradients.":[138],"Through":[139],"AGL,":[140],"degradation":[142],"can":[143],"mitigated":[145],"because":[146],"some":[147],"gradients":[148],"maintain":[149],"their":[150],"original":[151],"direction,":[152],"AGL":[154],"also":[155],"minimizes":[156],"accuracy":[158,202,228],"loss":[159],"perfectly":[161],"converging":[162],"which":[165],"require":[166],"pruning,":[167],"single":[170],"point.":[171],"Finally,":[172],"demonstrate":[174],"superiority":[176],"proposed":[179],"method":[180,193],"various":[182],"datasets":[183],"networks.":[185],"In":[186],"particular,":[187],"ILSVRC-2012":[190],"dataset,":[191],"our":[192],"52.09%":[195],"FLOPs":[196],"negligible":[199],"0.15%":[200],"top-1":[201],"ResNet-50.":[205],"As":[206],"result,":[208],"most":[212],"outstanding":[213],"compared":[215],"those":[217],"reported":[218],"previous":[220],"studies":[221],"terms":[223],"trade-off":[226],"between":[227],"complexity.":[231]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":23},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":4}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}