{"id":"https://openalex.org/W3089552161","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207259","title":"Pruning Depthwise Separable Convolutions for MobileNet Compression","display_name":"Pruning Depthwise Separable Convolutions for MobileNet Compression","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3089552161","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207259","mag":"3089552161"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9207259","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207259","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5077935756","display_name":"Cheng-Hao Tu","orcid":"https://orcid.org/0000-0002-3168-7963"},"institutions":[{"id":"https://openalex.org/I4210098366","display_name":"Institute of Information Science, Academia Sinica","ror":"https://ror.org/00z83z196","country_code":"TW","type":"facility","lineage":["https://openalex.org/I4210098366","https://openalex.org/I84653119"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Cheng-Hao Tu","raw_affiliation_strings":["Institute of Information Science, Academia Sinica, Taipei, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Information Science, Academia Sinica, Taipei, Taiwan","institution_ids":["https://openalex.org/I4210098366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064922699","display_name":"Jia\u2010Hong Lee","orcid":"https://orcid.org/0000-0001-8549-2793"},"institutions":[{"id":"https://openalex.org/I4210098366","display_name":"Institute of Information Science, Academia Sinica","ror":"https://ror.org/00z83z196","country_code":"TW","type":"facility","lineage":["https://openalex.org/I4210098366","https://openalex.org/I84653119"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Jia-Hong Lee","raw_affiliation_strings":["Institute of Information Science, Academia Sinica, Taipei, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Information Science, Academia Sinica, Taipei, Taiwan","institution_ids":["https://openalex.org/I4210098366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070424910","display_name":"Yi-Ming Chan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210098366","display_name":"Institute of Information Science, Academia Sinica","ror":"https://ror.org/00z83z196","country_code":"TW","type":"facility","lineage":["https://openalex.org/I4210098366","https://openalex.org/I84653119"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yi-Ming Chan","raw_affiliation_strings":["Institute of Information Science, Academia Sinica, Taipei, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Information Science, Academia Sinica, Taipei, Taiwan","institution_ids":["https://openalex.org/I4210098366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072890368","display_name":"Chu\u2010Song Chen","orcid":"https://orcid.org/0000-0002-2959-2471"},"institutions":[{"id":"https://openalex.org/I4210086894","display_name":"Research Center for Information Technology Innovation, Academia Sinica","ror":"https://ror.org/000zgvm20","country_code":"TW","type":"facility","lineage":["https://openalex.org/I4210086894","https://openalex.org/I84653119"]},{"id":"https://openalex.org/I4210098366","display_name":"Institute of Information Science, Academia Sinica","ror":"https://ror.org/00z83z196","country_code":"TW","type":"facility","lineage":["https://openalex.org/I4210098366","https://openalex.org/I84653119"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chu-Song Chen","raw_affiliation_strings":["Institute of Information Science, Academia Sinica MOST Joint Research Center for AI Technology and All Vista Healthcare, Taipei, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Information Science, Academia Sinica MOST Joint Research Center for AI Technology and All Vista Healthcare, Taipei, Taiwan","institution_ids":["https://openalex.org/I4210086894","https://openalex.org/I4210098366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"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":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":0.9994000196456909,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9980000257492065,"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/speedup","display_name":"Speedup","score":0.914354681968689},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.8747239112854004},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7963155508041382},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6848828196525574},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6313306093215942},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5193904042243958},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5089175701141357},{"id":"https://openalex.org/keywords/compression-ratio","display_name":"Compression ratio","score":0.47043487429618835},{"id":"https://openalex.org/keywords/separable-space","display_name":"Separable space","score":0.42472678422927856},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.39484134316444397},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3391858637332916},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.25305116176605225},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14911258220672607}],"concepts":[{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.914354681968689},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.8747239112854004},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7963155508041382},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6848828196525574},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6313306093215942},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5193904042243958},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5089175701141357},{"id":"https://openalex.org/C25797200","wikidata":"https://www.wikidata.org/wiki/Q828137","display_name":"Compression ratio","level":3,"score":0.47043487429618835},{"id":"https://openalex.org/C70710897","wikidata":"https://www.wikidata.org/wiki/Q680081","display_name":"Separable space","level":2,"score":0.42472678422927856},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39484134316444397},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3391858637332916},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.25305116176605225},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14911258220672607},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C511840579","wikidata":"https://www.wikidata.org/wiki/Q12757","display_name":"Internal combustion engine","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9207259","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207259","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5799999833106995,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W1650736245","https://openalex.org/W1665214252","https://openalex.org/W1667652561","https://openalex.org/W1821462560","https://openalex.org/W1836465849","https://openalex.org/W2117539524","https://openalex.org/W2148461049","https://openalex.org/W2155893237","https://openalex.org/W2163605009","https://openalex.org/W2276892413","https://openalex.org/W2335728318","https://openalex.org/W2400429454","https://openalex.org/W2612445135","https://openalex.org/W2630837129","https://openalex.org/W2764043458","https://openalex.org/W2962851801","https://openalex.org/W2962965870","https://openalex.org/W2963163009","https://openalex.org/W2963277998","https://openalex.org/W2963363373","https://openalex.org/W2963419583","https://openalex.org/W2963504571","https://openalex.org/W2963674932","https://openalex.org/W2963694111","https://openalex.org/W2963821229","https://openalex.org/W2963918968","https://openalex.org/W2964259004","https://openalex.org/W2970971581","https://openalex.org/W2976540144","https://openalex.org/W3106250896","https://openalex.org/W3118608800","https://openalex.org/W4295312788","https://openalex.org/W4297775537","https://openalex.org/W4302296459","https://openalex.org/W6637078681","https://openalex.org/W6637151318","https://openalex.org/W6637242042","https://openalex.org/W6638523607","https://openalex.org/W6638667902","https://openalex.org/W6681813608","https://openalex.org/W6684191040","https://openalex.org/W6703116779","https://openalex.org/W6726275242","https://openalex.org/W6737664043","https://openalex.org/W6739696289","https://openalex.org/W6745148473","https://openalex.org/W6746582238","https://openalex.org/W6756887525","https://openalex.org/W6766978945","https://openalex.org/W6785652829","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W2164382479","https://openalex.org/W3157543420","https://openalex.org/W2530952058","https://openalex.org/W2582836483","https://openalex.org/W3158431807","https://openalex.org/W4299366318","https://openalex.org/W4297580547","https://openalex.org/W2951583185","https://openalex.org/W4308155352"],"abstract_inverted_index":{"Deep":[0],"convolutional":[1],"neural":[2],"networks":[3],"are":[4],"good":[5,42],"at":[6,10,150],"accuracy":[7,143],"while":[8],"bad":[9],"efficiency.":[11],"To":[12],"improve":[13,38],"the":[14,23,39,72,82,104,111,114,120,129],"inference":[15],"speed,":[16],"two":[17],"directions":[18],"have":[19,34],"been":[20,35],"explored":[21],"in":[22,117],"past,":[24],"lightweight":[25],"model":[26,112],"designing":[27],"and":[28],"network":[29],"weight":[30,60],"pruning.":[31,61],"Lightweight":[32],"models":[33,58],"proposed":[36,121],"to":[37,69,97,102],"speed":[40,54,83],"with":[41,113,131,141],"enough":[43],"accuracy.":[44],"It":[45],"is,":[46],"however,":[47],"not":[48],"trivial":[49],"if":[50],"we":[51,65,95],"can":[52,126],"further":[53],"up":[55],"these":[56],"\"compact\"":[57],"by":[59],"In":[62],"this":[63,85],"paper,":[64],"present":[66],"a":[67,132],"technique":[68],"gradually":[70],"prune":[71,128],"depthwise":[73,92],"separable":[74,93],"convolution":[75],"networks,":[76],"such":[77],"as":[78],"MobileNet,":[79],"for":[80,145],"improving":[81],"of":[84,87,106,109],"kind":[86],"\"dense\"":[88],"network.":[89],"When":[90],"pruning":[91,110,124,134],"convolutions,":[94],"need":[96],"consider":[98],"more":[99],"structural":[100],"constraints":[101],"ensure":[103],"speedup":[105,140],"inference.":[107],"Instead":[108],"desired":[115],"ratio":[116],"one":[118],"stage,":[119],"multi-stage":[122],"gradual":[123],"approach":[125],"stably":[127],"filters":[130],"finer":[133],"ratio.":[135],"Our":[136],"method":[137],"achieves":[138],"satisfiable":[139],"little":[142],"drop":[144],"MobileNets.":[146],"Code":[147],"is":[148],"available":[149],"https://github.com/ivclab/Multistage_Pruning.":[151]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":5}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
