{"id":"https://openalex.org/W2922101623","doi":"https://doi.org/10.1117/12.2522992","title":"Deep convolutional neural networks compression method based on linear representation of kernels","display_name":"Deep convolutional neural networks compression method based on linear representation of kernels","publication_year":2019,"publication_date":"2019-03-15","ids":{"openalex":"https://openalex.org/W2922101623","doi":"https://doi.org/10.1117/12.2522992","mag":"2922101623"},"language":"en","primary_location":{"id":"doi:10.1117/12.2522992","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2522992","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Eleventh International Conference on Machine Vision (ICMV 2018)","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/A5046222208","display_name":"Ruobing Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ruobing Chen","raw_affiliation_strings":["Shanghai Jiao Tong Univ. (China)"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong Univ. (China)","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012756702","display_name":"Yefei Chen","orcid":"https://orcid.org/0000-0003-1414-2045"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yefei Chen","raw_affiliation_strings":["Shanghai Jiao Tong Univ. (China)"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong Univ. (China)","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084573070","display_name":"Jianbo Su","orcid":"https://orcid.org/0000-0001-6931-5842"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianbo Su","raw_affiliation_strings":["Shanghai Jiao Tong Univ. (China)"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong Univ. (China)","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5046222208"],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":0.2024,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.51298639,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"38.10","issue":null,"first_page":"73","last_page":"73"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.970300018787384,"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":0.970300018787384,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.797839879989624},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7661609053611755},{"id":"https://openalex.org/keywords/codebook","display_name":"Codebook","score":0.7649118900299072},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6430226564407349},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5782074928283691},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5606417059898376},{"id":"https://openalex.org/keywords/data-compression-ratio","display_name":"Data compression ratio","score":0.5495452284812927},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.4836364984512329},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.46695002913475037},{"id":"https://openalex.org/keywords/convolutional-code","display_name":"Convolutional code","score":0.44182586669921875},{"id":"https://openalex.org/keywords/compression","display_name":"Compression (physics)","score":0.42115071415901184},{"id":"https://openalex.org/keywords/image-compression","display_name":"Image compression","score":0.3605293035507202},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35426419973373413},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1871238648891449},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.10341626405715942},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.07837021350860596}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.797839879989624},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7661609053611755},{"id":"https://openalex.org/C127759330","wikidata":"https://www.wikidata.org/wiki/Q637416","display_name":"Codebook","level":2,"score":0.7649118900299072},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6430226564407349},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5782074928283691},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5606417059898376},{"id":"https://openalex.org/C94835093","wikidata":"https://www.wikidata.org/wiki/Q3113333","display_name":"Data compression ratio","level":5,"score":0.5495452284812927},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.4836364984512329},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.46695002913475037},{"id":"https://openalex.org/C157899210","wikidata":"https://www.wikidata.org/wiki/Q1395022","display_name":"Convolutional code","level":3,"score":0.44182586669921875},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.42115071415901184},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.3605293035507202},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35426419973373413},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1871238648891449},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.10341626405715942},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.07837021350860596},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"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/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2522992","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2522992","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Eleventh International Conference on Machine Vision (ICMV 2018)","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":38,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1798945469","https://openalex.org/W1821462560","https://openalex.org/W1836465849","https://openalex.org/W1903029394","https://openalex.org/W2097117768","https://openalex.org/W2104636679","https://openalex.org/W2108598243","https://openalex.org/W2112796928","https://openalex.org/W2119144962","https://openalex.org/W2131524184","https://openalex.org/W2163605009","https://openalex.org/W2172166488","https://openalex.org/W2177847924","https://openalex.org/W2194775991","https://openalex.org/W2279098554","https://openalex.org/W2300805302","https://openalex.org/W2302255633","https://openalex.org/W2329627532","https://openalex.org/W2513491952","https://openalex.org/W2544912866","https://openalex.org/W2612445135","https://openalex.org/W2701971652","https://openalex.org/W2724359148","https://openalex.org/W2739789140","https://openalex.org/W2739879705","https://openalex.org/W2741485222","https://openalex.org/W2748428003","https://openalex.org/W2750822049","https://openalex.org/W2766671879","https://openalex.org/W2796438033","https://openalex.org/W2963125010","https://openalex.org/W2963163009","https://openalex.org/W2963551763","https://openalex.org/W4206174637","https://openalex.org/W4241307704","https://openalex.org/W4297775537","https://openalex.org/W6638523607"],"related_works":["https://openalex.org/W98470534","https://openalex.org/W2126783397","https://openalex.org/W3206274587","https://openalex.org/W3165542721","https://openalex.org/W4378191574","https://openalex.org/W2119923792","https://openalex.org/W2161981399","https://openalex.org/W4243608781","https://openalex.org/W3117054490","https://openalex.org/W2387043686"],"abstract_inverted_index":{"Convolutional":[0],"Neural":[1],"Networks":[2],"(CNNs)":[3],"are":[4,74,96,122],"getting":[5],"larger":[6],"and":[7,9,118,138],"deeper,":[8],"thus":[10],"becoming":[11],"harder":[12],"to":[13,38,124,144,201],"be":[14,145,161,195],"deployed":[15],"on":[16,80,186],"systems":[17],"with":[18,84,134,183,197],"limited":[19],"resources.":[20],"Though":[21],"convolutional":[22,60,82,94,153,179],"filters":[23],"benefit":[24],"from":[25],"the":[26,43,93,99,130,191],"concept":[27],"of":[28,36,46,52,59,70,155,168,178],"receptive":[29],"field,":[30],"they":[31],"still":[32],"take":[33],"up":[34],"lots":[35],"resources":[37],"store":[39],"these":[40],"parameters":[41,117,142],"in":[42,64],"large":[44,157],"amounts":[45],"filters.":[47],"Therefore,":[48],"a":[49,68,106,119,126,156,165,175],"compression":[50,176,199,204],"method":[51,172,193],"pre-trained":[53],"CNN":[54,158],"models":[55],"using":[56,102,163],"\"Linear":[57],"Representation\"":[58],"kernels":[61,72,95,137,154],"is":[62,132],"introduced":[63],"this":[65,171],"paper.":[66],"First,":[67],"codebook":[69,120],"template":[71,136],"\"K<sub>t</sub>\".":[73],"generated":[75],"by":[76,98],"conducting":[77],"unsupervised":[78],"clustering":[79],"all":[81,92],"kernels,":[83],"Pearson":[85],"Correlation":[86],"Coefficient":[87],"set":[88],"as":[89],"distance.":[90],"Then":[91],"represented":[97,162],"closest":[100],"templates":[101],"linear":[103],"fitting":[104],"function":[105],"&bull;":[107],"K<sub>t</sub>":[108],"+":[109],"b":[110],",":[111],"which":[112],"means":[113],"that":[114,152],"only":[115,139,164],"two":[116,140],"index":[121],"enough":[123],"represent":[125],"kernel.":[127,149],"After":[128],"that,":[129],"model":[131,159],"retrained":[133],"fixed":[135],"related":[141],"need":[143],"finetuned":[146],"for":[147],"each":[148],"Experiments":[150],"show":[151],"can":[160,173,194],"small":[166],"amount":[167],"templates.":[169],"Thus,":[170],"reach":[174],"rate":[177],"layers":[180],"near":[181],"4&times;,":[182],"tiny":[184],"impact":[185],"precision":[187],"after":[188],"retraining.":[189],"Nevertheless,":[190],"proposed":[192],"performed":[196],"other":[198],"approaches":[200],"get":[202],"higher":[203],"rate.":[205]},"counts_by_year":[{"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"}