{"id":"https://openalex.org/W4229008806","doi":"https://doi.org/10.1145/3477314.3507089","title":"Practical and efficient out-of-domain detection with adversarial learning","display_name":"Practical and efficient out-of-domain detection with adversarial learning","publication_year":2022,"publication_date":"2022-04-25","ids":{"openalex":"https://openalex.org/W4229008806","doi":"https://doi.org/10.1145/3477314.3507089"},"language":"en","primary_location":{"id":"doi:10.1145/3477314.3507089","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3477314.3507089","pdf_url":null,"source":{"id":"https://openalex.org/S4363608665","display_name":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing","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/A5058155042","display_name":"Bo Wang","orcid":"https://orcid.org/0000-0001-7587-5141"},"institutions":[{"id":"https://openalex.org/I135598925","display_name":"Kyushu University","ror":"https://ror.org/00p4k0j84","country_code":"JP","type":"education","lineage":["https://openalex.org/I135598925"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Bo Wang","raw_affiliation_strings":["Kyushu University, Fukuoka City, Fukuoka, Japan"],"affiliations":[{"raw_affiliation_string":"Kyushu University, Fukuoka City, Fukuoka, Japan","institution_ids":["https://openalex.org/I135598925"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001584274","display_name":"Tsunenori Mine","orcid":"https://orcid.org/0000-0002-7462-8074"},"institutions":[{"id":"https://openalex.org/I135598925","display_name":"Kyushu University","ror":"https://ror.org/00p4k0j84","country_code":"JP","type":"education","lineage":["https://openalex.org/I135598925"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tsunenori Mine","raw_affiliation_strings":["Kyushu University, Fukuoka City, Fukuoka, Japan"],"affiliations":[{"raw_affiliation_string":"Kyushu University, Fukuoka City, Fukuoka, Japan","institution_ids":["https://openalex.org/I135598925"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5058155042"],"corresponding_institution_ids":["https://openalex.org/I135598925"],"apc_list":null,"apc_paid":null,"fwci":0.2079,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.37793579,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"853","last_page":"862"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9900000095367432,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9900000095367432,"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/T10260","display_name":"Software Engineering Research","score":0.9761000275611877,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11581","display_name":"Viral Infections and Outbreaks Research","score":0.9742000102996826,"subfield":{"id":"https://openalex.org/subfields/2725","display_name":"Infectious Diseases"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.8900452852249146},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.810755729675293},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8102360367774963},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6834962368011475},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6747699975967407},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6192781329154968},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5067029595375061},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.42576727271080017},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.06833595037460327}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8900452852249146},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.810755729675293},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8102360367774963},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6834962368011475},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6747699975967407},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6192781329154968},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5067029595375061},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.42576727271080017},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06833595037460327},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3477314.3507089","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3477314.3507089","pdf_url":null,"source":{"id":"https://openalex.org/S4363608665","display_name":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1832693441","https://openalex.org/W2575298912","https://openalex.org/W2747329762","https://openalex.org/W2889625178","https://openalex.org/W2891642103","https://openalex.org/W2898856000","https://openalex.org/W2912083425","https://openalex.org/W2962829230","https://openalex.org/W2963924212","https://openalex.org/W2986193249","https://openalex.org/W2990138404","https://openalex.org/W2997140799","https://openalex.org/W3014773921","https://openalex.org/W3034408878","https://openalex.org/W3100247553","https://openalex.org/W6701196884"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W4246396837","https://openalex.org/W2482350142","https://openalex.org/W3176240006","https://openalex.org/W3126451824","https://openalex.org/W1561927205","https://openalex.org/W3191453585","https://openalex.org/W4297672492","https://openalex.org/W4288019534","https://openalex.org/W4310988119"],"abstract_inverted_index":{"Detecting":[0],"Out-of-Domain":[1],"(OOD)":[2],"questions":[3,25],"is":[4,10],"important":[5],"in":[6,89,106],"real-world":[7],"applications":[8],"and":[9,48,59,112,125],"the":[11,52,57,90,96,107,148],"subject":[12],"of":[13,92,109,138,150],"active":[14],"research.":[15],"Traditional":[16],"In-Domain":[17],"(IND)":[18],"classifier-based":[19],"methods":[20,69,84],"can":[21],"easily":[22],"distinguish":[23],"OOD":[24,127],"if":[26],"they":[27],"are":[28,62,100],"very":[29,64],"different":[30],"from":[31],"IND":[32],"ones,":[33],"but":[34],"not":[35,71],"otherwise.":[36],"Although":[37],"large-scale":[38],"network-based":[39],"approaches":[40],"have":[41,85],"been":[42,86],"used":[43,88],"to":[44,73,154],"solve":[45],"this":[46,118],"problem":[47],"obtained":[49],"better":[50],"results,":[51],"computational":[53],"costs":[54],"associated":[55],"with":[56],"training":[58],"prediction":[60],"processes":[61],"usually":[63],"high.":[65],"In":[66,117],"addition,":[67],"these":[68,80],"did":[70],"seek":[72],"leverage":[74],"any":[75],"unlabeled":[76],"data.":[77],"To":[78],"address":[79],"issues,":[81],"adversarial":[82,132],"learning":[83],"widely":[87],"field":[91],"image":[93],"processing.":[94],"At":[95],"same":[97],"time,":[98],"there":[99],"currently":[101],"only":[102],"a":[103,122,136],"few":[104],"practices":[105],"domain":[108],"text":[110],"classification,":[111],"their":[113],"performance":[114],"remains":[115],"suboptimal.":[116],"paper,":[119],"we":[120],"propose":[121],"novel":[123],"practical":[124],"efficient":[126],"question":[128],"detection":[129],"framework":[130,152],"using":[131],"learning.":[133],"We":[134],"conducted":[135],"range":[137],"experiments":[139],"on":[140],"three":[141],"open":[142],"datasets.":[143],"The":[144],"experimental":[145],"results":[146],"demonstrate":[147],"advantages":[149],"our":[151],"compared":[153],"baseline":[155],"methods.":[156]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}