{"id":"https://openalex.org/W4415482491","doi":"https://doi.org/10.1109/access.2025.3624332","title":"GCSAM: Gradient Centralized Sharpness Aware Minimization","display_name":"GCSAM: Gradient Centralized Sharpness Aware Minimization","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415482491","doi":"https://doi.org/10.1109/access.2025.3624332","pmid":"https://pubmed.ncbi.nlm.nih.gov/41221151"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3624332","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3624332","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3624332","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112881727","display_name":"Mohamed Hassan","orcid":null},"institutions":[{"id":"https://openalex.org/I155093810","display_name":"University of Idaho","ror":"https://ror.org/03hbp5t65","country_code":"US","type":"education","lineage":["https://openalex.org/I155093810"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Mohamed Hassan","raw_affiliation_strings":["Department of Computer Science, University of Idaho, Idaho Falls, ID, USA"],"raw_orcid":"https://orcid.org/0009-0005-6934-1385","affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Idaho, Idaho Falls, ID, USA","institution_ids":["https://openalex.org/I155093810"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005267824","display_name":"Aleksandar Vakanski","orcid":"https://orcid.org/0000-0003-3365-1291"},"institutions":[{"id":"https://openalex.org/I155093810","display_name":"University of Idaho","ror":"https://ror.org/03hbp5t65","country_code":"US","type":"education","lineage":["https://openalex.org/I155093810"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aleksandar Vakanski","raw_affiliation_strings":["Department of Computer Science, University of Idaho, Idaho Falls, ID, USA"],"raw_orcid":"https://orcid.org/0000-0003-3365-1291","affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Idaho, Idaho Falls, ID, USA","institution_ids":["https://openalex.org/I155093810"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008484378","display_name":"Boyu Zhang","orcid":"https://orcid.org/0000-0002-9401-6163"},"institutions":[{"id":"https://openalex.org/I155093810","display_name":"University of Idaho","ror":"https://ror.org/03hbp5t65","country_code":"US","type":"education","lineage":["https://openalex.org/I155093810"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Boyu Zhang","raw_affiliation_strings":["Department of Computer Science, University of Idaho, Idaho Falls, ID, USA"],"raw_orcid":"https://orcid.org/0000-0002-9401-6163","affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Idaho, Idaho Falls, ID, USA","institution_ids":["https://openalex.org/I155093810"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080652935","display_name":"Min Xian","orcid":"https://orcid.org/0000-0001-6098-4441"},"institutions":[{"id":"https://openalex.org/I155093810","display_name":"University of Idaho","ror":"https://ror.org/03hbp5t65","country_code":"US","type":"education","lineage":["https://openalex.org/I155093810"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Min Xian","raw_affiliation_strings":["Department of Computer Science, University of Idaho, Idaho Falls, ID, USA"],"raw_orcid":"https://orcid.org/0000-0001-6098-4441","affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Idaho, Idaho Falls, ID, USA","institution_ids":["https://openalex.org/I155093810"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5112881727"],"corresponding_institution_ids":["https://openalex.org/I155093810"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.1982,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.83620053,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":"13","issue":null,"first_page":"182661","last_page":"182674"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9836999773979187,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9646000266075134,"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/generalization","display_name":"Generalization","score":0.7883999943733215},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5866000056266785},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5738999843597412},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.5601000189781189},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.49300000071525574},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.48010000586509705},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.46380001306533813},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4449000060558319},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.444599986076355}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.7883999943733215},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.779699981212616},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5866000056266785},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5738999843597412},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.5601000189781189},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.49300000071525574},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.48010000586509705},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4738999903202057},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.46380001306533813},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4449000060558319},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.444599986076355},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.43299999833106995},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42340001463890076},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.41019999980926514},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3986999988555908},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3862000107765198},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.3792000114917755},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3750999867916107},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.35109999775886536},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.34769999980926514},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3456000089645386},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.3416999876499176},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29499998688697815},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.2653999924659729},{"id":"https://openalex.org/C117765406","wikidata":"https://www.wikidata.org/wiki/Q5362437","display_name":"Generalization error","level":3,"score":0.25279998779296875},{"id":"https://openalex.org/C115680565","wikidata":"https://www.wikidata.org/wiki/Q5977448","display_name":"Gradient method","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/access.2025.3624332","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3624332","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmid:41221151","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41221151","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE access : practical innovations, open solutions","raw_type":null},{"id":"pmh:oai:europepmc.org:11410159","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12599882","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12599882/pdf/nihms-2120305.pdf","source":{"id":"https://openalex.org/S4306400806","display_name":"Europe PMC (PubMed Central)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1303153112","host_organization_name":"European Bioinformatics Institute","host_organization_lineage":["https://openalex.org/I1303153112"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:245e656f99e44c07a72864e9b5b6f9cf","is_oa":true,"landing_page_url":"https://doaj.org/article/245e656f99e44c07a72864e9b5b6f9cf","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 182661-182674 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3624332","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3624332","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3884834885","display_name":null,"funder_award_id":"P20GM104420","funder_id":"https://openalex.org/F4320337354","funder_display_name":"National Institute of General Medical Sciences"},{"id":"https://openalex.org/G5786885734","display_name":null,"funder_award_id":"P20GM104420","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"}],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337354","display_name":"National Institute of General Medical Sciences","ror":"https://ror.org/04q48ey07"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"generalization":[1,31,146,162],"performance":[2],"of":[3,26,55,145],"deep":[4],"neural":[5],"networks":[6],"(DNNs)":[7],"is":[8,163,172],"a":[9],"critical":[10,123],"factor":[11],"in":[12,29,143,158,166],"achieving":[13],"robust":[14,161],"model":[15],"behavior":[16],"on":[17,115],"unseen":[18],"data.":[19],"Recent":[20],"studies":[21],"have":[22],"highlighted":[23],"the":[24,53,56,101,140],"importance":[25],"sharpness-based":[27],"measures":[28],"promoting":[30],"by":[32],"encouraging":[33],"convergence":[34],"to":[35,68,91],"flatter":[36],"minima.":[37],"Among":[38],"these":[39,78],"approaches,":[40],"Sharpness-Aware":[41,83],"Minimization":[42,84],"(SAM)":[43],"has":[44],"emerged":[45],"as":[46],"an":[47],"effective":[48],"optimization":[49],"technique":[50],"for":[51,155],"reducing":[52,104],"sharpness":[54],"loss":[57],"landscape,":[58],"thereby":[59],"improving":[60,109,156],"generalization.":[61],"However,":[62],"SAM's":[63],"computational":[64,148],"overhead":[65],"and":[66,74,94,106,108,122,129,139,147],"sensitivity":[67],"noisy":[69],"gradients":[70,93,99],"limit":[71],"its":[72],"scalability":[73],"efficiency.":[75,149],"To":[76],"address":[77],"challenges,":[79],"we":[80],"propose":[81],"Gradient-Centralized":[82],"(GCSAM),":[85],"which":[86],"incorporates":[87],"Gradient":[88],"Centralization":[89],"(GC)":[90],"stabilize":[92],"accelerate":[95],"convergence.":[96],"GCSAM":[97,135],"normalizes":[98],"before":[100],"ascent":[102],"step,":[103],"noise":[105],"variance,":[107],"stability":[110],"during":[111],"training.":[112],"Our":[113,170],"evaluations":[114],"both":[116],"general":[117],"vision":[118],"benchmarks":[119],"(CIFAR-10,":[120],"CIFAR-100)":[121],"medical":[124,167],"imaging":[125],"datasets":[126],"(breast":[127],"ultrasound":[128],"COVID-19":[130],"chest":[131],"X-rays)":[132],"demonstrate":[133],"that":[134],"consistently":[136],"outperforms":[137],"SAM":[138],"Adam":[141],"optimizer":[142],"terms":[144],"These":[150],"results":[151],"highlight":[152],"GCSAM's":[153],"potential":[154],"reliability":[157],"domains":[159],"where":[160],"essential,":[164],"particularly":[165],"image":[168],"analysis.":[169],"code":[171],"available":[173],"at":[174],"https://github.com/mhassann22/GCSAM.":[175]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-24T00:00:00"}
