{"id":"https://openalex.org/W4283753285","doi":"https://doi.org/10.1142/s0218001422590285","title":"A New Network Pruning Framework Based on Rewind","display_name":"A New Network Pruning Framework Based on Rewind","publication_year":2022,"publication_date":"2022-07-01","ids":{"openalex":"https://openalex.org/W4283753285","doi":"https://doi.org/10.1142/s0218001422590285"},"language":"en","primary_location":{"id":"doi:10.1142/s0218001422590285","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218001422590285","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","raw_type":"journal-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/A5075599732","display_name":"Haoran Yu","orcid":"https://orcid.org/0000-0002-3344-7066"},"institutions":[{"id":"https://openalex.org/I119045251","display_name":"Huaqiao University","ror":"https://ror.org/03frdh605","country_code":"CN","type":"education","lineage":["https://openalex.org/I119045251"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoran Yu","raw_affiliation_strings":["College of Information Science and Engineering, HuaQiao University, Fujian, QuanZhou 362000, P. R. China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, HuaQiao University, Fujian, QuanZhou 362000, P. R. China","institution_ids":["https://openalex.org/I119045251"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100446757","display_name":"Weiwei Zhang","orcid":"https://orcid.org/0000-0002-7285-8714"},"institutions":[{"id":"https://openalex.org/I119045251","display_name":"Huaqiao University","ror":"https://ror.org/03frdh605","country_code":"CN","type":"education","lineage":["https://openalex.org/I119045251"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Weiwei Zhang","raw_affiliation_strings":["College of Engineering, HuaQiao University, Fujian, QuanZhou 362000, P. R. China"],"affiliations":[{"raw_affiliation_string":"College of Engineering, HuaQiao University, Fujian, QuanZhou 362000, P. R. China","institution_ids":["https://openalex.org/I119045251"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048897691","display_name":"Hongbo Zhou","orcid":"https://orcid.org/0000-0001-5920-468X"},"institutions":[{"id":"https://openalex.org/I119045251","display_name":"Huaqiao University","ror":"https://ror.org/03frdh605","country_code":"CN","type":"education","lineage":["https://openalex.org/I119045251"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongbo Zhou","raw_affiliation_strings":["College of Engineering, HuaQiao University, Fujian, QuanZhou 362000, P. R. China"],"affiliations":[{"raw_affiliation_string":"College of Engineering, HuaQiao University, Fujian, QuanZhou 362000, P. R. China","institution_ids":["https://openalex.org/I119045251"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100324443","display_name":"Xin Ma","orcid":"https://orcid.org/0000-0002-4743-5402"},"institutions":[{"id":"https://openalex.org/I119045251","display_name":"Huaqiao University","ror":"https://ror.org/03frdh605","country_code":"CN","type":"education","lineage":["https://openalex.org/I119045251"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Ma","raw_affiliation_strings":["College of Engineering, HuaQiao University, Fujian, QuanZhou 362000, P. R. China"],"affiliations":[{"raw_affiliation_string":"College of Engineering, HuaQiao University, Fujian, QuanZhou 362000, P. R. China","institution_ids":["https://openalex.org/I119045251"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100446757"],"corresponding_institution_ids":["https://openalex.org/I119045251"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0517145,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"36","issue":"12","first_page":null,"last_page":null},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9973000288009644,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9966999888420105,"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":0.814651608467102},{"id":"https://openalex.org/keywords/flops","display_name":"FLOPS","score":0.7827498912811279},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.7724559307098389},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5674340724945068},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.566010594367981},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4638090133666992},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42825862765312195},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3930646479129791},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32652202248573303},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14367887377738953},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.07786110043525696}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.814651608467102},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.7827498912811279},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.7724559307098389},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5674340724945068},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.566010594367981},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4638090133666992},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42825862765312195},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3930646479129791},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32652202248573303},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14367887377738953},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.07786110043525696},{"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/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0218001422590285","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218001422590285","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6963955736","display_name":null,"funder_award_id":"61976098","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1677182931","https://openalex.org/W2102605133","https://openalex.org/W2194775991","https://openalex.org/W2300242332","https://openalex.org/W2302255633","https://openalex.org/W2520760693","https://openalex.org/W2559655401","https://openalex.org/W2604272474","https://openalex.org/W2962851801","https://openalex.org/W2963446712","https://openalex.org/W3203376206","https://openalex.org/W4205955343"],"related_works":["https://openalex.org/W4315697128","https://openalex.org/W3102845713","https://openalex.org/W2971502891","https://openalex.org/W3205506801","https://openalex.org/W4280599700","https://openalex.org/W3183570023","https://openalex.org/W4382323155","https://openalex.org/W2016508734","https://openalex.org/W4287067436","https://openalex.org/W2986126107"],"abstract_inverted_index":{"Model":[0],"pruning":[1,18,34,46,86],"is":[2,23,68,91,102,211,219],"one":[3],"of":[4,8,27,75,114,157,172],"the":[5,15,30,33,40,44,52,88,99,112,126,131,140,146,153,158,170,181,184,199,205,208,212,217,220,229],"main":[6],"methods":[7,19],"deep":[9],"neural":[10,84],"network":[11,53,85,101,141],"model":[12,17],"compression.":[13],"However,":[14],"existing":[16],"are":[20],"inefficient,":[21],"there":[22],"still":[24],"a":[25,36,60,72,82,95,106,121,191],"lot":[26],"redundancy":[28],"in":[29],"network,":[31],"and":[32,57,98,135,174,183,216,227],"has":[35,59],"great":[37],"impact":[38],"on":[39,63,139],"accuracy.":[41,76,147],"In":[42,77,148],"addition,":[43,149],"traditional":[45],"process":[47],"usually":[48],"needs":[49],"to":[50,54,70,124,144],"fine-tune":[51],"restore":[55,125,145],"accuracy,":[56,127,182],"fine-tuning":[58],"limited":[61],"effect":[62,195],"accuracy":[64,193,210,218],"recovery,":[65],"so":[66],"it":[67,138],"difficult":[69],"achieve":[71],"high":[73],"level":[74],"this":[78,118],"paper,":[79],"we":[80,150,202],"propose":[81],"new":[83],"framework:":[87],"channel":[89],"sparsity":[90],"realized":[92],"by":[93,104,188],"introducing":[94],"scale":[96],"factor,":[97],"sparse":[100],"pruned":[103],"setting":[105],"global":[107],"threshold,":[108],"which":[109],"greatly":[110],"improves":[111],"efficiency":[113],"pruning.":[115],"After":[116],"pruning,":[117],"paper":[119],"proposes":[120],"rewind":[122,155,185,214],"method":[123,167,186],"that":[128,165,204],"is,":[129],"save":[130],"weight":[132,226],"after":[133,222],"training,":[134],"then":[136],"reload":[137],"for":[142],"training":[143],"also":[151],"study":[152],"best":[154,213],"point":[156],"three":[159],"networks.":[160],"The":[161],"experimental":[162],"results":[163],"show":[164],"our":[166],"significantly":[168],"reduces":[169],"number":[171],"parameters":[173],"FLOPs":[175],"without":[176],"affecting":[177],"or":[178],"even":[179],"improving":[180],"proposed":[187],"us":[189],"achieves":[190],"better":[192],"recovery":[194],"than":[196],"fine-tuning.":[197],"At":[198],"same":[200],"time,":[201],"find":[203],"epoch":[206],"with":[207],"highest":[209,221],"point,":[215],"saving":[223],"its":[224],"corresponding":[225],"retraining":[228],"model.":[230]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}