{"id":"https://openalex.org/W2765359583","doi":"https://doi.org/10.1145/3144789.3144803","title":"Compressing Deep Convolutional Networks Using K-means Based on Weights Distribution","display_name":"Compressing Deep Convolutional Networks Using K-means Based on Weights Distribution","publication_year":2017,"publication_date":"2017-07-17","ids":{"openalex":"https://openalex.org/W2765359583","doi":"https://doi.org/10.1145/3144789.3144803","mag":"2765359583"},"language":"en","primary_location":{"id":"doi:10.1145/3144789.3144803","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3144789.3144803","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Conference on Intelligent Information Processing","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":null,"display_name":"Wang Lei","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]},{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wang Lei","raw_affiliation_strings":["School of Aeronautics & Astronautics, University of Electronic Science and Technology of China, ChengDu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Aeronautics & Astronautics, University of Electronic Science and Technology of China, ChengDu, China","institution_ids":["https://openalex.org/I9842412","https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100704485","display_name":"Huawei Chen","orcid":"https://orcid.org/0000-0002-5020-3012"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]},{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huawei Chen","raw_affiliation_strings":["School of Aeronautics & Astronautics, University of Electronic Science and Technology of China, ChengDu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Aeronautics & Astronautics, University of Electronic Science and Technology of China, ChengDu, China","institution_ids":["https://openalex.org/I9842412","https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102892459","display_name":"Yixuan Wu","orcid":"https://orcid.org/0009-0004-9526-3221"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]},{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yixuan Wu","raw_affiliation_strings":["School of Aeronautics & Astronautics, University of Electronic Science and Technology of China, ChengDu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Aeronautics & Astronautics, University of Electronic Science and Technology of China, ChengDu, China","institution_ids":["https://openalex.org/I9842412","https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0924,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.48352566,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9965000152587891,"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/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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8430853486061096},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.763311505317688},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6893950700759888},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.5147757530212402},{"id":"https://openalex.org/keywords/compression","display_name":"Compression (physics)","score":0.48424193263053894},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.48319211602211},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44317418336868286},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.43458667397499084},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42896419763565063},{"id":"https://openalex.org/keywords/compression-ratio","display_name":"Compression ratio","score":0.42373302578926086},{"id":"https://openalex.org/keywords/weight-distribution","display_name":"Weight distribution","score":0.4163358807563782},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.41324150562286377},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3942187428474426},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3538823127746582},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.05740204453468323}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8430853486061096},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.763311505317688},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6893950700759888},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.5147757530212402},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.48424193263053894},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.48319211602211},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44317418336868286},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43458667397499084},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42896419763565063},{"id":"https://openalex.org/C25797200","wikidata":"https://www.wikidata.org/wiki/Q828137","display_name":"Compression ratio","level":3,"score":0.42373302578926086},{"id":"https://openalex.org/C25432639","wikidata":"https://www.wikidata.org/wiki/Q3711790","display_name":"Weight distribution","level":2,"score":0.4163358807563782},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.41324150562286377},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3942187428474426},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3538823127746582},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.05740204453468323},{"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/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","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},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3144789.3144803","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3144789.3144803","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Conference on Intelligent Information Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1845051632","https://openalex.org/W1981276685","https://openalex.org/W1998739300","https://openalex.org/W2073459066","https://openalex.org/W2102605133","https://openalex.org/W2107878631","https://openalex.org/W2119144962","https://openalex.org/W2150593711","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2199495299","https://openalex.org/W2233116163","https://openalex.org/W2279098554","https://openalex.org/W2559655401","https://openalex.org/W2593390416","https://openalex.org/W2597851033","https://openalex.org/W2618530766","https://openalex.org/W2962835968","https://openalex.org/W4236385439"],"related_works":["https://openalex.org/W1512321724","https://openalex.org/W4231317009","https://openalex.org/W2367696392","https://openalex.org/W4383723869","https://openalex.org/W4293703255","https://openalex.org/W2161302774","https://openalex.org/W2388481516","https://openalex.org/W2271907651","https://openalex.org/W3007688875","https://openalex.org/W4384298135"],"abstract_inverted_index":{"For":[0],"the":[1,18,35,41,51,104,108,124,150],"application":[2,151],"of":[3,34,53,152],"deep":[4],"neural":[5,68,154],"networks":[6,155],"on":[7,112,119,156],"devices":[8],"with":[9,93],"limited":[10],"hardware":[11],"resources,":[12],"it":[13],"is":[14,25,38],"necessary":[15],"to":[16,29,39,43,64,79,89,122,132,140],"reduce":[17,50],"computational":[19],"complexity":[20],"and":[21,86,102,134],"storage":[22],"requirement.":[23],"Compression":[24],"an":[26,59],"effective":[27],"way":[28,148],"achieve":[30],"this":[31,82,97],"goal.":[32],"One":[33],"available":[36],"method":[37],"quantize":[40,80,123],"weights":[42,71,120,125],"enforce":[44],"weight":[45],"sharing,":[46],"which":[47],"can":[48,99,126],"greatly":[49],"parameters":[52],"each":[54],"layer.":[55],"This":[56,143],"paper":[57],"presents":[58],"improved":[60],"k-means":[61,117],"clustering":[62,77],"algorithm":[63,83,98,144],"compress":[65,90],"CNN":[66],"(convolutional":[67],"networks).By":[69],"taking":[70],"distribution":[72,121],"into":[73],"consideration":[74],"when":[75],"choosing":[76],"centers":[78,88],"weights,":[81],"automatically":[84],"chooses":[85],"revises":[87],"network.":[91],"Compared":[92],"traditional":[94,141],"quantification":[95],"method,":[96],"maintain":[100],"accuracy":[101,136],"increase":[103],"compression":[105,128],"speed":[106,129],"at":[107],"same":[109],"time.":[110],"Experiments":[111],"AlexNet":[113],"show":[114],"that":[115],"using":[116],"based":[118],"improve":[127,135],"by":[130,137],"5%":[131],"10%":[133],"6%":[138],"compared":[139],"algorithm.":[142],"provides":[145],"a":[146],"better":[147],"for":[149],"convolutional":[153],"mobile":[157],"devices.":[158]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}