{"id":"https://openalex.org/W2990555391","doi":"https://doi.org/10.1109/avss.2019.8909826","title":"Information theory based pruning for CNN compression and its application to image classification and action recognition","display_name":"Information theory based pruning for CNN compression and its application to image classification and action recognition","publication_year":2019,"publication_date":"2019-09-01","ids":{"openalex":"https://openalex.org/W2990555391","doi":"https://doi.org/10.1109/avss.2019.8909826","mag":"2990555391"},"language":"en","primary_location":{"id":"doi:10.1109/avss.2019.8909826","is_oa":false,"landing_page_url":"https://doi.org/10.1109/avss.2019.8909826","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","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/A5073390553","display_name":"Hai\u2010Hong Phan","orcid":"https://orcid.org/0000-0002-7886-7128"},"institutions":[{"id":"https://openalex.org/I2800379142","display_name":"Universit\u00e9 Paris-Seine","ror":"https://ror.org/0033vhx30","country_code":"FR","type":"education","lineage":["https://openalex.org/I2800379142"]},{"id":"https://openalex.org/I4210142324","display_name":"CY Cergy Paris Universit\u00e9","ror":"https://ror.org/043htjv09","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210142324"]},{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"funder","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I86175216","display_name":"\u00c9cole Nationale Sup\u00e9rieure de l'\u00c9lectronique et de ses Applications","ror":"https://ror.org/03qeacd72","country_code":"FR","type":"education","lineage":["https://openalex.org/I86175216"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Hai-Hong Phan","raw_affiliation_strings":["ENSEA, CNRS, ETIS/Universit\u00e9 Paris Seine, Universit\u00e9 Cergy-Pontoise, France"],"affiliations":[{"raw_affiliation_string":"ENSEA, CNRS, ETIS/Universit\u00e9 Paris Seine, Universit\u00e9 Cergy-Pontoise, France","institution_ids":["https://openalex.org/I2800379142","https://openalex.org/I86175216","https://openalex.org/I4210142324","https://openalex.org/I1294671590"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044294351","display_name":"Ngoc\u2010Son Vu","orcid":"https://orcid.org/0000-0003-3498-3335"},"institutions":[{"id":"https://openalex.org/I2800379142","display_name":"Universit\u00e9 Paris-Seine","ror":"https://ror.org/0033vhx30","country_code":"FR","type":"education","lineage":["https://openalex.org/I2800379142"]},{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"funder","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210142324","display_name":"CY Cergy Paris Universit\u00e9","ror":"https://ror.org/043htjv09","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210142324"]},{"id":"https://openalex.org/I86175216","display_name":"\u00c9cole Nationale Sup\u00e9rieure de l'\u00c9lectronique et de ses Applications","ror":"https://ror.org/03qeacd72","country_code":"FR","type":"education","lineage":["https://openalex.org/I86175216"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Ngoc-Son Vu","raw_affiliation_strings":["ENSEA, CNRS, ETIS/Universit\u00e9 Paris Seine, Universit\u00e9 Cergy-Pontoise, France"],"affiliations":[{"raw_affiliation_string":"ENSEA, CNRS, ETIS/Universit\u00e9 Paris Seine, Universit\u00e9 Cergy-Pontoise, France","institution_ids":["https://openalex.org/I2800379142","https://openalex.org/I86175216","https://openalex.org/I4210142324","https://openalex.org/I1294671590"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5073390553"],"corresponding_institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I2800379142","https://openalex.org/I4210142324","https://openalex.org/I86175216"],"apc_list":null,"apc_paid":null,"fwci":0.2024,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.55402211,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"abs 1704 4861","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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":1.0,"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":0.9998000264167786,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9990000128746033,"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":0.8468288779258728},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.7864950895309448},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7296743988990784},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6085233688354492},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5827681422233582},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5728740096092224},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.5659065246582031},{"id":"https://openalex.org/keywords/memory-footprint","display_name":"Memory footprint","score":0.5452088713645935},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.4760865271091461},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.44694942235946655},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42054614424705505},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.39356186985969543},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3394498825073242}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8468288779258728},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.7864950895309448},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7296743988990784},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6085233688354492},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5827681422233582},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5728740096092224},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.5659065246582031},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.5452088713645935},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.4760865271091461},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.44694942235946655},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42054614424705505},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.39356186985969543},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3394498825073242},{"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},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"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/avss.2019.8909826","is_oa":false,"landing_page_url":"https://doi.org/10.1109/avss.2019.8909826","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.4699999988079071,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W24089286","https://openalex.org/W639708223","https://openalex.org/W1532826273","https://openalex.org/W1935978687","https://openalex.org/W1996901117","https://openalex.org/W2097117768","https://openalex.org/W2112796928","https://openalex.org/W2117539524","https://openalex.org/W2155893237","https://openalex.org/W2161591461","https://openalex.org/W2163605009","https://openalex.org/W2167215970","https://openalex.org/W2194775991","https://openalex.org/W2279098554","https://openalex.org/W2462996230","https://openalex.org/W2515385951","https://openalex.org/W2551895583","https://openalex.org/W2553910756","https://openalex.org/W2559965405","https://openalex.org/W2612445135","https://openalex.org/W2613718673","https://openalex.org/W2707890836","https://openalex.org/W2771111398","https://openalex.org/W2890788829","https://openalex.org/W2950248853","https://openalex.org/W2952899695","https://openalex.org/W2962944050","https://openalex.org/W2962965870","https://openalex.org/W2962988160","https://openalex.org/W2963173190","https://openalex.org/W2963674932","https://openalex.org/W2963897760","https://openalex.org/W2964081807","https://openalex.org/W2964233199","https://openalex.org/W2964299589","https://openalex.org/W3098951351","https://openalex.org/W4297775537","https://openalex.org/W6631798071","https://openalex.org/W6640289440","https://openalex.org/W6683826617","https://openalex.org/W6684191040","https://openalex.org/W6684563725","https://openalex.org/W6685627644","https://openalex.org/W6729763630","https://openalex.org/W6737664043"],"related_works":["https://openalex.org/W2950475743","https://openalex.org/W4386603768","https://openalex.org/W2886711096","https://openalex.org/W3088108839","https://openalex.org/W992687842","https://openalex.org/W4300560302","https://openalex.org/W4387163578","https://openalex.org/W4206451978","https://openalex.org/W3195622388","https://openalex.org/W3041443116"],"abstract_inverted_index":{"Convolutional":[0],"neural":[1],"networks":[2,46],"(CNNs)":[3],"have":[4],"become":[5],"the":[6,76,80,90,99,107,135,176],"power":[7],"method":[8,112,132],"for":[9,42,51,156,169],"many":[10],"computer":[11],"vision":[12,55],"applications,":[13],"including":[14],"image":[15,157],"classification":[16,158],"and":[17,25,33,53,79,101,104,120,124,141,154,164],"action":[18,171],"recognition.":[19],"However,":[20],"they":[21],"are":[22,29],"almost":[23],"computationally":[24],"memory":[26,82],"intensive,":[27],"thus":[28,142],"challenging":[30],"to":[31,34,70,74,88,115,139],"use":[32],"deploy":[35],"on":[36,95,118,148,159],"systems":[37],"with":[38],"limited":[39],"resources,":[40],"except":[41],"a":[43,66,86],"few":[44],"recent":[45],"which":[47],"were":[48],"specifically":[49],"designed":[50],"mobile":[52],"embedded":[54],"applications":[56],"such":[57],"as":[58,166,168],"MobileNet,":[59],"NASNet-Mobile.":[60],"In":[61],"this":[62],"paper,":[63],"we":[64],"present":[65],"novel":[67],"efficient":[68],"algorithm":[69],"compress":[71],"CNN":[72,150],"models":[73,151],"decrease":[75],"computational":[77],"cost":[78],"run-time":[81],"footprint.":[83],"We":[84],"propose":[85],"strategy":[87],"measure":[89],"redundancy":[91],"of":[92],"parameters":[93],"based":[94],"their":[96],"relationship":[97],"using":[98],"covariance":[100],"correlation":[102],"criteria,":[103],"then":[105],"prune":[106],"less":[108],"important":[109],"ones.":[110],"Our":[111],"directly":[113],"applies":[114],"CNNs,":[116],"both":[117],"convolutional":[119],"fully":[121],"connected":[122],"layers,":[123],"requires":[125],"no":[126],"specialized":[127],"software/hardware":[128],"accelerators.":[129],"The":[130],"proposed":[131],"significantly":[133],"reduces":[134],"model":[136],"sizes":[137],"(up":[138],"70%)":[140],"computing":[143],"costs":[144],"without":[145],"performance":[146],"loss":[147],"different":[149,160],"(AlexNet,":[152],"ResNet,":[153],"LeNet)":[155],"datasets":[161],"(MNIST,":[162],"CIFAR10,":[163],"ImageNet)":[165],"well":[167],"human":[170],"recognition":[172],"(on":[173],"dataset":[174],"like":[175],"UCF101).":[177]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}