{"id":"https://openalex.org/W2978903092","doi":"https://doi.org/10.1109/ijcnn.2019.8852347","title":"Depersonalized Cross-Subject Vigilance Estimation with Adversarial Domain Generalization","display_name":"Depersonalized Cross-Subject Vigilance Estimation with Adversarial Domain Generalization","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2978903092","doi":"https://doi.org/10.1109/ijcnn.2019.8852347","mag":"2978903092"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8852347","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852347","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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/A5088372143","display_name":"Bo-Qun Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bo-Qun Ma","raw_affiliation_strings":["Department of Computer Science and Engineering, Center for Brain-like Computing and Machine Intelligence"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Center for Brain-like Computing and Machine Intelligence","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100317846","display_name":"He Li","orcid":"https://orcid.org/0000-0002-0655-1634"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He Li","raw_affiliation_strings":["Department of Computer Science and Engineering, Center for Brain-like Computing and Machine Intelligence"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Center for Brain-like Computing and Machine Intelligence","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071796684","display_name":"Yun Luo","orcid":"https://orcid.org/0000-0001-7780-4929"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yun Luo","raw_affiliation_strings":["Department of Computer Science and Engineering, Center for Brain-like Computing and Machine Intelligence"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Center for Brain-like Computing and Machine Intelligence","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040440605","display_name":"Bao\u2010Liang Lu","orcid":"https://orcid.org/0000-0001-8359-0058"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bao-Liang Lu","raw_affiliation_strings":["Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5088372143"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0864,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.7722077,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12597","display_name":"Fire Detection and Safety Systems","score":0.9940999746322632,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12597","display_name":"Fire Detection and Safety Systems","score":0.9940999746322632,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9713000059127808,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.9387999773025513,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6967471837997437},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6374483108520508},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5678431987762451},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.5426486134529114},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5011129379272461},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4896610379219055},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.48354458808898926},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19646385312080383},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.14005908370018005}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6967471837997437},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6374483108520508},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5678431987762451},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.5426486134529114},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5011129379272461},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4896610379219055},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.48354458808898926},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19646385312080383},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.14005908370018005},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2019.8852347","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852347","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W96659543","https://openalex.org/W1541210109","https://openalex.org/W1731081199","https://openalex.org/W1852255964","https://openalex.org/W1920962657","https://openalex.org/W1947251450","https://openalex.org/W1977882209","https://openalex.org/W1982696459","https://openalex.org/W2005305331","https://openalex.org/W2025623975","https://openalex.org/W2037347936","https://openalex.org/W2051218759","https://openalex.org/W2056545070","https://openalex.org/W2090158744","https://openalex.org/W2093792557","https://openalex.org/W2115403315","https://openalex.org/W2118585731","https://openalex.org/W2122098299","https://openalex.org/W2124610677","https://openalex.org/W2126151240","https://openalex.org/W2135378997","https://openalex.org/W2155858138","https://openalex.org/W2165698076","https://openalex.org/W2167366427","https://openalex.org/W2174958781","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2407512993","https://openalex.org/W2558193840","https://openalex.org/W2578674746","https://openalex.org/W2579628011","https://openalex.org/W2593768305","https://openalex.org/W2897051344","https://openalex.org/W3124617164","https://openalex.org/W6637618735","https://openalex.org/W6677656871","https://openalex.org/W6678977525","https://openalex.org/W6683124652","https://openalex.org/W6713572877","https://openalex.org/W6732468801"],"related_works":["https://openalex.org/W4300172004","https://openalex.org/W2955455867","https://openalex.org/W3203792196","https://openalex.org/W4375869316","https://openalex.org/W4321649381","https://openalex.org/W3180787869","https://openalex.org/W2997645659","https://openalex.org/W4295929828","https://openalex.org/W2250728308","https://openalex.org/W3156096827"],"abstract_inverted_index":{"Subject":[0],"variability":[1],"is":[2,34],"a":[3,89,100,131],"major":[4],"obstacle":[5],"to":[6,14,39],"vigilance":[7,60],"estimation.":[8],"The":[9,20],"conventional":[10],"subject-specific":[11,30],"models":[12,62,169],"fail":[13],"perform":[15,171],"well":[16,172],"on":[17,25,130,173],"unknown":[18,44,69,156,178],"subjects.":[19,70,179],"existing":[21],"studies":[22],"mainly":[23],"focus":[24],"domain":[26,55,102,163,167],"adaptation":[27,164],"utilizing":[28],"labeled/unlabeled":[29],"data.":[31],"However,":[32],"it":[33],"still":[35],"expensive":[36],"and":[37,86,119],"inconvenient":[38],"collect":[40],"task-specific":[41],"data":[42],"from":[43,67,154],"subjects":[45,157],"in":[46,158],"some":[47],"real-world":[48],"applications.":[49],"In":[50,113],"this":[51],"paper,":[52],"we":[53,122],"introduce":[54],"generalization":[56,103,120,168],"methods":[57],"for":[58,127],"building":[59],"estimation":[61,117],"without":[63,150],"requiring":[64],"any":[65],"information":[66,153],"the":[68,74,116,155,161,174],"We":[71,98],"first":[72],"generalize":[73],"structure":[75,92],"of":[76,115],"Domain":[77,83,94],"Adversarial":[78],"Neural":[79],"Network":[80,96],"(DANN)":[81],"into":[82],"Generalization":[84],"(DG-DANN),":[85],"then":[87],"propose":[88],"novel":[90],"adversarial":[91],"called":[93,134],"Residual":[95],"(DResNet).":[97],"compare":[99],"popular":[101],"method,":[104],"Domain-Invariant":[105],"Component":[106],"Analysis":[107],"(DICA),":[108],"with":[109,160,176],"our":[110,140],"proposed":[111],"approach.":[112],"terms":[114],"accuracy":[118,145],"ability,":[121],"designed":[123],"two":[124],"different":[125],"settings":[126],"evaluation":[128],"experiments":[129],"public":[132],"dataset":[133],"SEED-VIG.":[135],"Experimental":[136],"results":[137],"indicate":[138],"that":[139],"new":[141],"model":[142],"achieves":[143],"comparable":[144],"but":[146],"more":[147],"stable":[148],"performance":[149],"using":[151],"additional":[152],"comparison":[159],"state-of-the-art":[162],"methods.":[165],"Furthermore,":[166],"also":[170],"tasks":[175],"multiple":[177]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
