{"id":"https://openalex.org/W4292402825","doi":"https://doi.org/10.1109/access.2022.3196780","title":"Multiexpert Adversarial Regularization for Robust and Data-Efficient Deep Supervised Learning","display_name":"Multiexpert Adversarial Regularization for Robust and Data-Efficient Deep Supervised Learning","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4292402825","doi":"https://doi.org/10.1109/access.2022.3196780"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3196780","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3196780","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09853519.pdf","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"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09853519.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024312006","display_name":"Behnam Gholami","orcid":"https://orcid.org/0000-0003-0338-1921"},"institutions":[{"id":"https://openalex.org/I4210101778","display_name":"Samsung (United States)","ror":"https://ror.org/01bfbvm65","country_code":"US","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210101778"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Behnam Gholami","raw_affiliation_strings":["SOC Cellular and Multimedia Research and Development Laboratory, Samsung Semiconductor Inc., San Diego, CA, USA","SOC Cellular and Multimedia Lab R&#x0026"],"raw_orcid":"https://orcid.org/0000-0003-0338-1921","affiliations":[{"raw_affiliation_string":"SOC Cellular and Multimedia Research and Development Laboratory, Samsung Semiconductor Inc., San Diego, CA, USA","institution_ids":["https://openalex.org/I4210101778"]},{"raw_affiliation_string":"SOC Cellular and Multimedia Lab R&#x0026","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019919993","display_name":"Qingfeng Liu","orcid":"https://orcid.org/0000-0003-1313-9418"},"institutions":[{"id":"https://openalex.org/I4210101778","display_name":"Samsung (United States)","ror":"https://ror.org/01bfbvm65","country_code":"US","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210101778"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qingfeng Liu","raw_affiliation_strings":["SOC Cellular and Multimedia Research and Development Laboratory, Samsung Semiconductor Inc., San Diego, CA, USA","SOC Cellular and Multimedia Lab R&#x0026"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SOC Cellular and Multimedia Research and Development Laboratory, Samsung Semiconductor Inc., San Diego, CA, USA","institution_ids":["https://openalex.org/I4210101778"]},{"raw_affiliation_string":"SOC Cellular and Multimedia Lab R&#x0026","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022805456","display_name":"Mostafa El\u2010Khamy","orcid":"https://orcid.org/0000-0001-9421-6037"},"institutions":[{"id":"https://openalex.org/I4210101778","display_name":"Samsung (United States)","ror":"https://ror.org/01bfbvm65","country_code":"US","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210101778"]},{"id":"https://openalex.org/I84524832","display_name":"Alexandria University","ror":"https://ror.org/00mzz1w90","country_code":"EG","type":"education","lineage":["https://openalex.org/I84524832"]}],"countries":["EG","US"],"is_corresponding":false,"raw_author_name":"Mostafa El-Khamy","raw_affiliation_strings":["SOC Cellular and Multimedia Research and Development Laboratory, Samsung Semiconductor Inc., San Diego, CA, USA","Faculty of Engineering, Alexandria University, Alexandria, Egypt","SOC Cellular and Multimedia Lab R&#x0026"],"raw_orcid":"https://orcid.org/0000-0001-9421-6037","affiliations":[{"raw_affiliation_string":"SOC Cellular and Multimedia Research and Development Laboratory, Samsung Semiconductor Inc., San Diego, CA, USA","institution_ids":["https://openalex.org/I4210101778"]},{"raw_affiliation_string":"Faculty of Engineering, Alexandria University, Alexandria, Egypt","institution_ids":["https://openalex.org/I84524832"]},{"raw_affiliation_string":"SOC Cellular and Multimedia Lab R&#x0026","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100331815","display_name":"Jungwon Lee","orcid":"https://orcid.org/0000-0001-8922-063X"},"institutions":[{"id":"https://openalex.org/I4210101778","display_name":"Samsung (United States)","ror":"https://ror.org/01bfbvm65","country_code":"US","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210101778"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jungwon Lee","raw_affiliation_strings":["SOC Cellular and Multimedia Research and Development Laboratory, Samsung Semiconductor Inc., San Diego, CA, USA","SOC Cellular and Multimedia Lab R&#x0026"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SOC Cellular and Multimedia Research and Development Laboratory, Samsung Semiconductor Inc., San Diego, CA, USA","institution_ids":["https://openalex.org/I4210101778"]},{"raw_affiliation_string":"SOC Cellular and Multimedia Lab R&#x0026","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08960053,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"10","issue":null,"first_page":"85080","last_page":"85094"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998000264167786,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998000264167786,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.852139949798584},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7816634178161621},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7280264496803284},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7126030325889587},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6740450263023376},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5159071683883667},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.50528484582901},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.5026857852935791},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4969365894794464},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.49420860409736633},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.4717458188533783},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4560203552246094},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4471547305583954},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.39320045709609985},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.11721095442771912}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.852139949798584},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7816634178161621},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7280264496803284},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7126030325889587},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6740450263023376},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5159071683883667},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.50528484582901},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.5026857852935791},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4969365894794464},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.49420860409736633},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.4717458188533783},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4560203552246094},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4471547305583954},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39320045709609985},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.11721095442771912},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2022.3196780","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3196780","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09853519.pdf","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":"pmh:oai:doaj.org/article:30553d53b0a64e29b573dc36966e739d","is_oa":true,"landing_page_url":"https://doaj.org/article/30553d53b0a64e29b573dc36966e739d","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 10, Pp 85080-85094 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3196780","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3196780","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09853519.pdf","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":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4292402825.pdf","grobid_xml":"https://content.openalex.org/works/W4292402825.grobid-xml"},"referenced_works_count":87,"referenced_works":["https://openalex.org/W1563795667","https://openalex.org/W1945616565","https://openalex.org/W2031342017","https://openalex.org/W2061119986","https://openalex.org/W2107641306","https://openalex.org/W2108598243","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2340897893","https://openalex.org/W2431080869","https://openalex.org/W2460583509","https://openalex.org/W2531327146","https://openalex.org/W2746314669","https://openalex.org/W2765407302","https://openalex.org/W2913314773","https://openalex.org/W2943642558","https://openalex.org/W2950787360","https://openalex.org/W2951940613","https://openalex.org/W2951970475","https://openalex.org/W2952217990","https://openalex.org/W2954996726","https://openalex.org/W2955425717","https://openalex.org/W2962369866","https://openalex.org/W2963238274","https://openalex.org/W2975221988","https://openalex.org/W2976816191","https://openalex.org/W2982225063","https://openalex.org/W2992308087","https://openalex.org/W2994088087","https://openalex.org/W2996501936","https://openalex.org/W2996564870","https://openalex.org/W2997564604","https://openalex.org/W3001197829","https://openalex.org/W3005680577","https://openalex.org/W3009184501","https://openalex.org/W3014641072","https://openalex.org/W3034831986","https://openalex.org/W3035437864","https://openalex.org/W3035452548","https://openalex.org/W3035682985","https://openalex.org/W3036224891","https://openalex.org/W3092095626","https://openalex.org/W3098341014","https://openalex.org/W3106753828","https://openalex.org/W3109301572","https://openalex.org/W3118608800","https://openalex.org/W3128325039","https://openalex.org/W4288363831","https://openalex.org/W4288404646","https://openalex.org/W4293846201","https://openalex.org/W4294611718","https://openalex.org/W4295727797","https://openalex.org/W4297665946","https://openalex.org/W6633802082","https://openalex.org/W6640425456","https://openalex.org/W6717772578","https://openalex.org/W6728622933","https://openalex.org/W6730042731","https://openalex.org/W6739868092","https://openalex.org/W6743428213","https://openalex.org/W6745136726","https://openalex.org/W6748353538","https://openalex.org/W6751795773","https://openalex.org/W6753695549","https://openalex.org/W6757555829","https://openalex.org/W6758857762","https://openalex.org/W6762619590","https://openalex.org/W6762718338","https://openalex.org/W6763996246","https://openalex.org/W6764051988","https://openalex.org/W6764192614","https://openalex.org/W6764945797","https://openalex.org/W6765939562","https://openalex.org/W6767312631","https://openalex.org/W6767545298","https://openalex.org/W6770979763","https://openalex.org/W6771378132","https://openalex.org/W6771787070","https://openalex.org/W6771809012","https://openalex.org/W6772167297","https://openalex.org/W6773005947","https://openalex.org/W6774314701","https://openalex.org/W6775845032","https://openalex.org/W6779997284","https://openalex.org/W6780651745","https://openalex.org/W6787972765","https://openalex.org/W6791063394"],"related_works":["https://openalex.org/W2950183588","https://openalex.org/W3080754722","https://openalex.org/W4383221314","https://openalex.org/W3093978547","https://openalex.org/W2953536436","https://openalex.org/W3203790781","https://openalex.org/W4313346231","https://openalex.org/W2738001131","https://openalex.org/W4285785480","https://openalex.org/W2997056298"],"abstract_inverted_index":{"Deep":[0],"neural":[1],"networks":[2,166],"(DNNs)":[3],"can":[4],"achieve":[5],"high":[6],"accuracy":[7],"when":[8],"there":[9],"is":[10,28,51,195],"abundant":[11],"training":[12,43,56],"data":[13,26,76],"that":[14,193],"has":[15],"the":[16,20,35,42,52,55,58,70,102,120,124,131,143,149,152],"same":[17],"distribution":[18],"as":[19,146,148],"test":[21,59,161],"data.":[22],"In":[23,81],"practical":[24],"applications,":[25],"deficiency":[27],"often":[29,45],"a":[30,86,184],"concern.":[31],"For":[32],"classification":[33,176],"tasks,":[34,174],"lack":[36],"of":[37,106,123,151,186],"enough":[38],"labeled":[39],"images":[40],"in":[41,47,63,134],"set":[44],"results":[46,62],"overfitting.":[48],"Another":[49],"issue":[50],"mismatch":[53],"between":[54],"and":[57,75,104,177,200],"domains,":[60],"which":[61],"poor":[64],"model":[65],"performance.":[66],"This":[67],"calls":[68],"for":[69,158,169],"need":[71],"to":[72,100,119,129,154,183],"have":[73],"robust":[74,157,205],"efficient":[77],"deep":[78,87,107],"learning":[79,88,94,109,204],"models.":[80,110],"this":[82],"work,":[83],"we":[84],"propose":[85],"approach":[89],"called":[90],"Multi-Expert":[91],"Adversarial":[92],"Regularization":[93],"(MEAR)":[95],"with":[96,167,197],"limited":[97],"computational":[98],"overhead":[99],"improve":[101],"generalization":[103],"robustness":[105],"supervised":[108],"The":[111],"MEAR":[112,127,168,182,194],"framework":[113],"appends":[114],"multiple":[115,189],"classifier":[116],"heads":[117],"(experts)":[118],"feature":[121,132],"extractor":[122,133],"legacy":[125],"model.":[126],"aims":[128],"learn":[130],"an":[135,159],"adversarial":[136],"fashion":[137],"by":[138],"leveraging":[139],"complementary":[140],"information":[141],"from":[142],"individual":[144],"experts":[145,153],"well":[147],"ensemble":[150],"be":[155],"more":[156,201],"unseen":[160],"domain.":[162],"We":[163,180,191],"train":[164],"state-of-the-art":[165],"two":[170],"important":[171],"computer":[172],"vision":[173],"image":[175],"semantic":[178],"segmentation.":[179],"compare":[181],"variety":[185],"baselines":[187],"on":[188],"benchmarks.":[190],"show":[192],"competitive":[196],"other":[198],"methods":[199],"successful":[202],"at":[203],"features.":[206]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}