{"id":"https://openalex.org/W4387760935","doi":"https://doi.org/10.1109/ipccc59175.2023.10253832","title":"A Model Compression Method Using Significant Data and Knowledge Distillation","display_name":"A Model Compression Method Using Significant Data and Knowledge Distillation","publication_year":2023,"publication_date":"2023-10-18","ids":{"openalex":"https://openalex.org/W4387760935","doi":"https://doi.org/10.1109/ipccc59175.2023.10253832"},"language":"en","primary_location":{"id":"doi:10.1109/ipccc59175.2023.10253832","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ipccc59175.2023.10253832","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Performance, Computing, and Communications Conference (IPCCC)","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/A5100403380","display_name":"Fan Zhang","orcid":"https://orcid.org/0000-0001-6087-8243"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fan Zhang","raw_affiliation_strings":["Zhejiang University,ZhouShan,China","Zhejiang University, ZhouShan, China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University,ZhouShan,China","institution_ids":["https://openalex.org/I76130692"]},{"raw_affiliation_string":"Zhejiang University, ZhouShan, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101860666","display_name":"Beibei Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I134687103","display_name":"Nanjing Tech University","ror":"https://ror.org/03sd35x91","country_code":"CN","type":"education","lineage":["https://openalex.org/I134687103"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Beibei Xu","raw_affiliation_strings":["Nanjing Tech University,NanJing,China","Nanjing Tech University, NanJing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing Tech University,NanJing,China","institution_ids":["https://openalex.org/I134687103"]},{"raw_affiliation_string":"Nanjing Tech University, NanJing, China","institution_ids":["https://openalex.org/I134687103"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101562353","display_name":"Kai Cui","orcid":"https://orcid.org/0000-0001-9806-7112"},"institutions":[{"id":"https://openalex.org/I134687103","display_name":"Nanjing Tech University","ror":"https://ror.org/03sd35x91","country_code":"CN","type":"education","lineage":["https://openalex.org/I134687103"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Cui","raw_affiliation_strings":["Nanjing Tech University,NanJing,China","Nanjing Tech University, NanJing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing Tech University,NanJing,China","institution_ids":["https://openalex.org/I134687103"]},{"raw_affiliation_string":"Nanjing Tech University, NanJing, China","institution_ids":["https://openalex.org/I134687103"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100403380"],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13354737,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"20","issue":null,"first_page":"481","last_page":"487"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.5573999881744385,"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"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.5573999881744385,"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/overfitting","display_name":"Overfitting","score":0.9072674512863159},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8100011944770813},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5781792998313904},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5753722786903381},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5541839599609375},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5451717376708984},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.5273828506469727},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5140751600265503},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.481020987033844},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.47592881321907043},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.45770904421806335},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.4531377851963043},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.45209720730781555},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.44882211089134216},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4429125189781189},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.44077861309051514},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.09674733877182007},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07458198070526123}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.9072674512863159},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8100011944770813},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5781792998313904},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5753722786903381},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5541839599609375},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5451717376708984},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.5273828506469727},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5140751600265503},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.481020987033844},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.47592881321907043},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.45770904421806335},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.4531377851963043},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.45209720730781555},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.44882211089134216},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4429125189781189},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44077861309051514},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.09674733877182007},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07458198070526123},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"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:10.1109/ipccc59175.2023.10253832","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ipccc59175.2023.10253832","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Performance, Computing, and Communications Conference (IPCCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1690739335","https://openalex.org/W1814328102","https://openalex.org/W1821462560","https://openalex.org/W2194775991","https://openalex.org/W2593501769","https://openalex.org/W2896457183","https://openalex.org/W2949605285","https://openalex.org/W2963723401","https://openalex.org/W3034223443","https://openalex.org/W3095319910","https://openalex.org/W3101656801","https://openalex.org/W3118608800","https://openalex.org/W3175930218","https://openalex.org/W4239196604","https://openalex.org/W4283761305","https://openalex.org/W4287065492","https://openalex.org/W4385245566","https://openalex.org/W6637551013","https://openalex.org/W6638523607","https://openalex.org/W6735632633","https://openalex.org/W6745722055","https://openalex.org/W6754204458","https://openalex.org/W6755207826","https://openalex.org/W6786215913","https://openalex.org/W6799502010","https://openalex.org/W6839990677"],"related_works":["https://openalex.org/W4318142952","https://openalex.org/W4312531262","https://openalex.org/W2072070946","https://openalex.org/W3132346564","https://openalex.org/W2991483587","https://openalex.org/W2786391746","https://openalex.org/W2914559142","https://openalex.org/W4226059458","https://openalex.org/W4381430104","https://openalex.org/W2995102745"],"abstract_inverted_index":{"With":[0],"the":[1,8,12,19,21,23,37,53,56,76,97,104,125,133,136,143,149,153,164,168,177,180,182,188],"emergence":[2],"of":[3,11,39,55,78,135,152,187],"complex":[4],"neural":[5],"network":[6],"models,":[7],"running":[9,145,185],"cost":[10,146],"models":[13],"shows":[14],"an":[15,72],"increasing":[16],"trend,":[17],"while":[18,147],"larger":[20],"model,":[22,138,155,181],"exponential":[24],"increase":[25],"in":[26,93],"training":[27,79,88,99,122,134,183],"cost.":[28,65],"Therefore,":[29],"this":[30],"paper":[31],"proposes":[32],"a":[33,158],"method":[34,111,159],"to":[35,74,118,128,131,141],"compress":[36],"model":[38,58,94,127,144,150,189],"Convolutional":[40],"Neural":[41],"Network":[42],"(CNN)":[43],"by":[44],"combining":[45],"data":[46,68,80,89,100,114,123,169,178],"selection":[47],"and":[48,85,156,179,184],"knowledge":[49,109,130],"distillation,":[50],"so":[51,139],"that":[52],"performance":[54,95,151],"original":[57],"can":[59,160,190],"be":[60,191],"maintained":[61],"with":[62,90,112],"low":[63,113],"computational":[64],"(1)":[66],"For":[67],"selection,":[69],"we":[70],"propose":[71],"algorithm":[73],"rank":[75],"importance":[77],"based":[81,102],"on":[82,103],"influence":[83],"scores,":[84],"select":[86],"some":[87,120],"greater":[91],"improvement":[92],"as":[96,140],"new":[98],"set":[101],"ranking":[105],"results.":[106],"(2)":[107],"A":[108],"distillation":[110],"volume":[115,170],"is":[116,171],"proposed":[117],"input":[119],"important":[121],"into":[124],"teacher":[126,154],"generate":[129],"guide":[132],"student":[137],"reduce":[142],"maintaining":[148],"such":[157],"also":[161],"availably":[162],"avoid":[163],"overfitting":[165],"phenomenon":[166],"when":[167],"too":[172],"small.":[173],"By":[174],"compressing":[175],"both":[176],"costs":[186],"effectively":[192],"reduced.":[193]},"counts_by_year":[],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}
