{"id":"https://openalex.org/W3007122219","doi":"https://doi.org/10.1109/jstsp.2020.2975987","title":"Discriminative Layer Pruning for Convolutional Neural Networks","display_name":"Discriminative Layer Pruning for Convolutional Neural Networks","publication_year":2020,"publication_date":"2020-02-25","ids":{"openalex":"https://openalex.org/W3007122219","doi":"https://doi.org/10.1109/jstsp.2020.2975987","mag":"3007122219"},"language":"en","primary_location":{"id":"doi:10.1109/jstsp.2020.2975987","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstsp.2020.2975987","pdf_url":null,"source":{"id":"https://openalex.org/S42167783","display_name":"IEEE Journal of Selected Topics in Signal Processing","issn_l":"1932-4553","issn":["1932-4553","1941-0484"],"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 Journal of Selected Topics in Signal Processing","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/A5112859940","display_name":"Artur Jord\u00e3o","orcid":null},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Artur Jordao","raw_affiliation_strings":["Department of Computer Science, Smart Sense Laboratory, Federal University of Minas Gerais, Belo Horizonte, Brazil"],"raw_orcid":"https://orcid.org/0000-0002-3503-3019","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Smart Sense Laboratory, Federal University of Minas Gerais, Belo Horizonte, Brazil","institution_ids":["https://openalex.org/I110200422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016348186","display_name":"Maiko Lie","orcid":"https://orcid.org/0000-0003-4190-2556"},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Maiko Lie","raw_affiliation_strings":["Department of Computer Science, Smart Sense Laboratory, Federal University of Minas Gerais, Belo Horizonte, Brazil"],"raw_orcid":"https://orcid.org/0000-0003-4190-2556","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Smart Sense Laboratory, Federal University of Minas Gerais, Belo Horizonte, Brazil","institution_ids":["https://openalex.org/I110200422"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044741265","display_name":"William Robson Schwartz","orcid":"https://orcid.org/0000-0003-1449-8834"},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"William Robson Schwartz","raw_affiliation_strings":["Department of Computer Science, Smart Sense Laboratory, Federal University of Minas Gerais, Belo Horizonte, Brazil"],"raw_orcid":"https://orcid.org/0000-0003-1449-8834","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Smart Sense Laboratory, Federal University of Minas Gerais, Belo Horizonte, Brazil","institution_ids":["https://openalex.org/I110200422"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.643,"has_fulltext":false,"cited_by_count":43,"citation_normalized_percentile":{"value":0.91575376,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"14","issue":"4","first_page":"828","last_page":"837"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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":0.9997000098228455,"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/T10320","display_name":"Neural Networks and Applications","score":0.9973999857902527,"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/T12676","display_name":"Machine Learning and ELM","score":0.9962999820709229,"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":0.8052414059638977},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.8046635389328003},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.6887449026107788},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5927860736846924},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5312387943267822},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.507342517375946},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4897298812866211},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.45257988572120667},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.42049989104270935},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.41252970695495605},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3964523673057556},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35906779766082764}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8052414059638977},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8046635389328003},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6887449026107788},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5927860736846924},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5312387943267822},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.507342517375946},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4897298812866211},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.45257988572120667},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.42049989104270935},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.41252970695495605},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3964523673057556},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35906779766082764},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/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/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jstsp.2020.2975987","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstsp.2020.2975987","pdf_url":null,"source":{"id":"https://openalex.org/S42167783","display_name":"IEEE Journal of Selected Topics in Signal Processing","issn_l":"1932-4553","issn":["1932-4553","1941-0484"],"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 Journal of Selected Topics in Signal Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.5}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":86,"referenced_works":["https://openalex.org/W1026270304","https://openalex.org/W1581066146","https://openalex.org/W1686810756","https://openalex.org/W1836465849","https://openalex.org/W1996195892","https://openalex.org/W2009441118","https://openalex.org/W2022466155","https://openalex.org/W2097117768","https://openalex.org/W2114766824","https://openalex.org/W2117539524","https://openalex.org/W2119144962","https://openalex.org/W2123582174","https://openalex.org/W2125389748","https://openalex.org/W2146656095","https://openalex.org/W2154579312","https://openalex.org/W2163605009","https://openalex.org/W2171658832","https://openalex.org/W2194775991","https://openalex.org/W2256782633","https://openalex.org/W2267635276","https://openalex.org/W2276892413","https://openalex.org/W2495425901","https://openalex.org/W2534262995","https://openalex.org/W2553303224","https://openalex.org/W2612445135","https://openalex.org/W2618530766","https://openalex.org/W2623451521","https://openalex.org/W2736953746","https://openalex.org/W2739542029","https://openalex.org/W2805003733","https://openalex.org/W2808483802","https://openalex.org/W2884751099","https://openalex.org/W2885527679","https://openalex.org/W2889744664","https://openalex.org/W2897139873","https://openalex.org/W2899090668","https://openalex.org/W2949941638","https://openalex.org/W2950621961","https://openalex.org/W2962746461","https://openalex.org/W2962755847","https://openalex.org/W2962835968","https://openalex.org/W2962944050","https://openalex.org/W2962965870","https://openalex.org/W2963094099","https://openalex.org/W2963140066","https://openalex.org/W2963145730","https://openalex.org/W2963363373","https://openalex.org/W2963374479","https://openalex.org/W2963393494","https://openalex.org/W2963446712","https://openalex.org/W2963516298","https://openalex.org/W2963674932","https://openalex.org/W2963813662","https://openalex.org/W2964019666","https://openalex.org/W2964062240","https://openalex.org/W2964233199","https://openalex.org/W2964299589","https://openalex.org/W2964350391","https://openalex.org/W3118608800","https://openalex.org/W4289305285","https://openalex.org/W4289378385","https://openalex.org/W4295262505","https://openalex.org/W4297775537","https://openalex.org/W6626481562","https://openalex.org/W6634833660","https://openalex.org/W6637373629","https://openalex.org/W6638667902","https://openalex.org/W6677103964","https://openalex.org/W6677580257","https://openalex.org/W6678583879","https://openalex.org/W6682751323","https://openalex.org/W6693397755","https://openalex.org/W6694260854","https://openalex.org/W6723181079","https://openalex.org/W6726275242","https://openalex.org/W6729956949","https://openalex.org/W6737664043","https://openalex.org/W6739513683","https://openalex.org/W6741753902","https://openalex.org/W6751979845","https://openalex.org/W6753490951","https://openalex.org/W6755034786","https://openalex.org/W6755737735","https://openalex.org/W6756025085","https://openalex.org/W6766225098","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W147410782","https://openalex.org/W3022252430","https://openalex.org/W4287804464","https://openalex.org/W3103989898","https://openalex.org/W3211292372","https://openalex.org/W803346624"],"abstract_inverted_index":{"The":[0],"predictive":[1],"ability":[2,192],"of":[3,25,43,140,146,186,193,209,217],"convolutional":[4,90],"neural":[5],"networks":[6,49],"(CNNs)":[7],"can":[8,133],"be":[9,134],"improved":[10],"by":[11,114,120],"increasing":[12,16],"their":[13],"depth.":[14,95],"However,":[15],"depth":[17,151],"also":[18,109],"increases":[19],"computational":[20],"cost":[21],"significantly,":[22],"in":[23],"terms":[24],"both":[26],"floating":[27],"point":[28],"operations":[29],"and":[30,41,220],"memory":[31,106],"consumption,":[32],"hindering":[33],"applicability":[34],"on":[35,99,161],"resource-constrained":[36],"systems":[37],"such":[38],"as":[39],"mobile":[40],"internet":[42],"things":[44],"(IoT)":[45],"devices.":[46],"Fortunately,":[47],"most":[48],"have":[50,62],"spare":[51],"capacity,":[52],"that":[53,127,173,198,221,229,242],"is,":[54],"they":[55,60],"require":[56],"fewer":[57],"parameters":[58,75],"than":[59],"actually":[61],"to":[63,76,92,136,148,183,189,214,240],"perform":[64],"accurately.":[65],"This":[66],"motivates":[67],"network":[68,94,141,155,212],"compression":[69],"methods,":[70],"which":[71],"remove":[72],"or":[73],"quantize":[74],"improve":[77],"resource-efficiency.":[78],"In":[79],"this":[80,101,178],"work,":[81],"we":[82,227],"consider":[83],"a":[84,128,149,153,162,169],"straightforward":[85],"strategy":[86],"for":[87],"removing":[88],"entire":[89],"layers":[91],"reduce":[93],"Since":[96],"it":[97,222],"focuses":[98],"depth,":[100],"approach":[102,132,172],"not":[103],"only":[104],"reduces":[105,110],"usage,":[107],"but":[108],"prediction":[111],"time":[112],"significantly":[113],"mitigating":[115],"the":[116,138,144,184,187,190,194,207,210],"serialization":[117],"overhead":[118],"incurred":[119],"forwarding":[121],"through":[122],"consecutive":[123],"layers.":[124,247],"We":[125,158,196],"show":[126,197,228],"simple":[129],"subspace":[130,163],"projection":[131,171],"employed":[135],"estimate":[137,159],"importance":[139,160,179],"layers,":[142],"enabling":[143],"pruning":[145,202,205,232],"CNNs":[147],"resource-efficient":[150],"within":[152],"given":[154],"size":[156],"constraint.":[157],"computed":[164],"using":[165,215],"Partial":[166],"Least":[167],"Squares,":[168],"feature":[170],"preserves":[174],"discriminative":[175,200,230],"information.":[176],"Consequently,":[177],"estimation":[180],"is":[181],"correlated":[182],"contribution":[185],"layer":[188,201,231],"classification":[191],"model.":[195],"cascading":[199],"with":[203],"filter-oriented":[204],"improves":[206],"resource-efficiency":[208,238],"resulting":[211],"compared":[213,239],"any":[216],"them":[218],"alone,":[219,233],"outperforms":[223],"state-of-the-art":[224],"methods.":[225],"Moreover,":[226],"without":[234],"cascading,":[235],"achieves":[236],"competitive":[237],"methods":[241],"prune":[243],"filters":[244],"from":[245],"all":[246]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
