{"id":"https://openalex.org/W4383501652","doi":"https://doi.org/10.1109/aicas57966.2023.10168584","title":"GPIL: Gradient with PseudoInverse Learning for High Accuracy Fine-Tuning","display_name":"GPIL: Gradient with PseudoInverse Learning for High Accuracy Fine-Tuning","publication_year":2023,"publication_date":"2023-06-11","ids":{"openalex":"https://openalex.org/W4383501652","doi":"https://doi.org/10.1109/aicas57966.2023.10168584"},"language":"en","primary_location":{"id":"doi:10.1109/aicas57966.2023.10168584","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/aicas57966.2023.10168584","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS)","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/A5104267519","display_name":"Gilha Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I118373667","display_name":"Seoul National University of Science and Technology","ror":"https://ror.org/00chfja07","country_code":"KR","type":"education","lineage":["https://openalex.org/I118373667"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Gilha Lee","raw_affiliation_strings":["Seoul National University of Science and Technology,Research Center for Electrical and Information Technology,Department of Electrical and Information Engineering,Seoul,Korea,01811"],"affiliations":[{"raw_affiliation_string":"Seoul National University of Science and Technology,Research Center for Electrical and Information Technology,Department of Electrical and Information Engineering,Seoul,Korea,01811","institution_ids":["https://openalex.org/I118373667"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110741547","display_name":"Nam Joon Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I118373667","display_name":"Seoul National University of Science and Technology","ror":"https://ror.org/00chfja07","country_code":"KR","type":"education","lineage":["https://openalex.org/I118373667"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Nam Joon Kim","raw_affiliation_strings":["Seoul National University of Science and Technology,Research Center for Electrical and Information Technology,Department of Electrical and Information Engineering,Seoul,Korea,01811"],"affiliations":[{"raw_affiliation_string":"Seoul National University of Science and Technology,Research Center for Electrical and Information Technology,Department of Electrical and Information Engineering,Seoul,Korea,01811","institution_ids":["https://openalex.org/I118373667"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100371185","display_name":"Hyun Kim","orcid":"https://orcid.org/0000-0002-7962-657X"},"institutions":[{"id":"https://openalex.org/I118373667","display_name":"Seoul National University of Science and Technology","ror":"https://ror.org/00chfja07","country_code":"KR","type":"education","lineage":["https://openalex.org/I118373667"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyun Kim","raw_affiliation_strings":["Seoul National University of Science and Technology,Research Center for Electrical and Information Technology,Department of Electrical and Information Engineering,Seoul,Korea,01811"],"affiliations":[{"raw_affiliation_string":"Seoul National University of Science and Technology,Research Center for Electrical and Information Technology,Department of Electrical and Information Engineering,Seoul,Korea,01811","institution_ids":["https://openalex.org/I118373667"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5104267519"],"corresponding_institution_ids":["https://openalex.org/I118373667"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06713322,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"7","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9993000030517578,"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/T12676","display_name":"Machine Learning and ELM","score":0.9987999796867371,"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/moore\u2013penrose-pseudoinverse","display_name":"Moore\u2013Penrose pseudoinverse","score":0.9549821615219116},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7166694402694702},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.5844080448150635},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5652471780776978},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.5639434456825256},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.49242910742759705},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4869779050350189},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.48503297567367554},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.4633719325065613},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4457872807979584},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.39034464955329895},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1852630376815796},{"id":"https://openalex.org/keywords/inverse","display_name":"Inverse","score":0.11991319060325623}],"concepts":[{"id":"https://openalex.org/C21556879","wikidata":"https://www.wikidata.org/wiki/Q43219517","display_name":"Moore\u2013Penrose pseudoinverse","level":3,"score":0.9549821615219116},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7166694402694702},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.5844080448150635},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5652471780776978},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.5639434456825256},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.49242910742759705},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4869779050350189},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.48503297567367554},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.4633719325065613},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4457872807979584},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39034464955329895},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1852630376815796},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.11991319060325623},{"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/aicas57966.2023.10168584","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/aicas57966.2023.10168584","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.49000000953674316,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W2108598243","https://openalex.org/W2111072639","https://openalex.org/W2125485651","https://openalex.org/W2165698076","https://openalex.org/W2193145675","https://openalex.org/W2194775991","https://openalex.org/W2551176409","https://openalex.org/W2786746258","https://openalex.org/W2899067142","https://openalex.org/W2935524202","https://openalex.org/W2963037989","https://openalex.org/W2963163009","https://openalex.org/W2976540144","https://openalex.org/W2981958729","https://openalex.org/W2982083293","https://openalex.org/W2993182889","https://openalex.org/W3082058622","https://openalex.org/W3100889249","https://openalex.org/W3106250896","https://openalex.org/W3118608800","https://openalex.org/W3122390253","https://openalex.org/W4282960063","https://openalex.org/W4285059684","https://openalex.org/W4294691336","https://openalex.org/W4312914306","https://openalex.org/W6748267448","https://openalex.org/W6755791423","https://openalex.org/W6785228155","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W2115605526","https://openalex.org/W3093883775","https://openalex.org/W1539246760","https://openalex.org/W2786746258","https://openalex.org/W4225893763","https://openalex.org/W2788727425","https://openalex.org/W2405196115","https://openalex.org/W2104893957","https://openalex.org/W4402471162","https://openalex.org/W2971074373"],"abstract_inverted_index":{"PseudoInverse":[0],"learning":[1],"(PIL)":[2],"is":[3,44,77,113,205],"proposed":[4,187,230],"to":[5,87,120,178,221,245],"increase":[6],"the":[7,59,73,94,97,109,157,163,167,173,181,186,197,229,233,239,246],"convergence":[8],"speed":[9],"of":[10,56,96,166,176,183],"conventional":[11],"gradient":[12,28,132],"descent.":[13],"PIL":[14,34,75,80,130],"can":[15],"be":[16,62,121],"trained":[17],"with":[18],"fast":[19],"and":[20,131,191],"reliable":[21],"convolutional":[22],"neural":[23],"networks":[24],"(CNNs)":[25],"without":[26],"a":[27,30,40,102,117,202],"using":[29,128],"pseudoinverse":[31,68,164,174],"matrix.":[32],"However,":[33],"has":[35,81],"several":[36],"problems":[37],"when":[38],"training":[39,199,223],"network.":[41],"First,":[42],"there":[43],"an":[45,218],"out-of-memory":[46],"problem":[47],"because":[48,64],"all":[49],"batches":[50],"are":[51,70],"required":[52],"during":[53],"one":[54],"epoch":[55],"training.":[57],"Second,":[58],"network":[60],"cannot":[61],"deeper":[63,74],"more":[65,123,151,192],"unreliable":[66],"input":[67],"matrices":[69],"used":[71,89,115],"as":[72,213],"layer":[76],"stacked.":[78],"Therefore,":[79],"not":[82,135],"yet":[83],"been":[84],"effectively":[85],"applied":[86],"widely":[88],"deep":[90],"models.":[91],"Inspired":[92],"by":[93,127,242],"limitation":[95],"existing":[98,158],"PIL,":[99,144,159],"we":[100,134,170],"propose":[101],"novel":[103],"error":[104,153,194],"propagation":[105,195],"methodology":[106],"that":[107,216,228],"allows":[108],"fine-tuning":[110,208,237],"process,":[111],"which":[112,140,160],"often":[114],"in":[116,143,196,209],"resource-constrained":[118,210],"environment,":[119],"performed":[122],"accurately.":[124],"In":[125],"detail,":[126],"both":[129],"descent,":[133],"only":[136,162],"enable":[137],"mini-batch":[138],"training,":[139],"was":[141],"impossible":[142],"but":[145],"also":[146],"achieve":[147],"higher":[148],"accuracy":[149,219,234],"through":[150],"accurate":[152,193],"propagation.":[154],"Moreover,":[155],"unlike":[156],"uses":[161],"matrix":[165,175],"CNN":[168,198],"input,":[169],"additionally":[171],"use":[172],"weights":[177],"compensate":[179],"for":[180,207],"limitations":[182],"PIL;":[184],"thus,":[185],"method":[188,231],"enables":[189],"faster":[190],"process.":[200],"As":[201],"result,":[203],"it":[204],"efficient":[206],"environments,":[211],"such":[212],"mobile/edge":[214],"devices":[215],"require":[217],"comparable":[220],"small":[222],"epochs.":[224],"Experimental":[225],"results":[226],"show":[227],"improves":[232],"after":[235],"ResNet-101":[236],"on":[238],"CIFAR-100":[240],"dataset":[241],"2.78%":[243],"compared":[244],"baseline.":[247]},"counts_by_year":[],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}