{"id":"https://openalex.org/W2921271542","doi":"https://doi.org/10.23919/eusipco.2019.8902738","title":"Open-Set Recognition Using Intra-Class Splitting","display_name":"Open-Set Recognition Using Intra-Class Splitting","publication_year":2019,"publication_date":"2019-09-01","ids":{"openalex":"https://openalex.org/W2921271542","doi":"https://doi.org/10.23919/eusipco.2019.8902738","mag":"2921271542"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco.2019.8902738","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco.2019.8902738","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 27th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1903.04774","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042000387","display_name":"Patrick Schlachter","orcid":"https://orcid.org/0000-0002-6051-5693"},"institutions":[{"id":"https://openalex.org/I100066346","display_name":"University of Stuttgart","ror":"https://ror.org/04vnq7t77","country_code":"DE","type":"education","lineage":["https://openalex.org/I100066346"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Patrick Schlachter","raw_affiliation_strings":["Institute of Signal Processing and System Theory, University of Stuttgart, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Signal Processing and System Theory, University of Stuttgart, Germany","institution_ids":["https://openalex.org/I100066346"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083549742","display_name":"Yi-Wen Liao","orcid":"https://orcid.org/0000-0002-2885-9824"},"institutions":[{"id":"https://openalex.org/I100066346","display_name":"University of Stuttgart","ror":"https://ror.org/04vnq7t77","country_code":"DE","type":"education","lineage":["https://openalex.org/I100066346"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Yiwen Liao","raw_affiliation_strings":["Institute of Signal Processing and System Theory, University of Stuttgart, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Signal Processing and System Theory, University of Stuttgart, Germany","institution_ids":["https://openalex.org/I100066346"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101717968","display_name":"Bin Yang","orcid":"https://orcid.org/0000-0002-8322-117X"},"institutions":[{"id":"https://openalex.org/I100066346","display_name":"University of Stuttgart","ror":"https://ror.org/04vnq7t77","country_code":"DE","type":"education","lineage":["https://openalex.org/I100066346"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Bin Yang","raw_affiliation_strings":["Institute of Signal Processing and System Theory, University of Stuttgart, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Signal Processing and System Theory, University of Stuttgart, Germany","institution_ids":["https://openalex.org/I100066346"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5042000387"],"corresponding_institution_ids":["https://openalex.org/I100066346"],"apc_list":null,"apc_paid":null,"fwci":2.6044,"has_fulltext":false,"cited_by_count":37,"citation_normalized_percentile":{"value":0.92012294,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9990000128746033,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9990000128746033,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9955000281333923,"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":0.991599977016449,"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/classifier","display_name":"Classifier (UML)","score":0.7103077173233032},{"id":"https://openalex.org/keywords/open-set","display_name":"Open set","score":0.7026276588439941},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7024273872375488},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6531338095664978},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6530472040176392},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.540972888469696},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5037857890129089},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4977276623249054},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.49033188819885254},{"id":"https://openalex.org/keywords/closed-set","display_name":"Closed set","score":0.48489171266555786},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.46017518639564514},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4293992519378662},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3452399969100952},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3398336172103882},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2960014045238495},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22750335931777954}],"concepts":[{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7103077173233032},{"id":"https://openalex.org/C42357961","wikidata":"https://www.wikidata.org/wiki/Q213363","display_name":"Open set","level":2,"score":0.7026276588439941},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7024273872375488},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6531338095664978},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6530472040176392},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.540972888469696},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5037857890129089},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4977276623249054},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.49033188819885254},{"id":"https://openalex.org/C164953516","wikidata":"https://www.wikidata.org/wiki/Q320357","display_name":"Closed set","level":2,"score":0.48489171266555786},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.46017518639564514},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4293992519378662},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3452399969100952},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3398336172103882},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2960014045238495},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22750335931777954},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.23919/eusipco.2019.8902738","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco.2019.8902738","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 27th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1903.04774","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1903.04774","pdf_url":"https://arxiv.org/pdf/1903.04774","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1903.04774","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1903.04774","pdf_url":"https://arxiv.org/pdf/1903.04774","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2018459374","https://openalex.org/W2097117768","https://openalex.org/W2099471712","https://openalex.org/W2100332042","https://openalex.org/W2101234009","https://openalex.org/W2101807845","https://openalex.org/W2112796928","https://openalex.org/W2117539524","https://openalex.org/W2119880843","https://openalex.org/W2132870739","https://openalex.org/W2194775991","https://openalex.org/W2335728318","https://openalex.org/W2402144811","https://openalex.org/W2895752198","https://openalex.org/W2901114541","https://openalex.org/W2912062932","https://openalex.org/W2919115771","https://openalex.org/W2922340657","https://openalex.org/W2953384591","https://openalex.org/W2962835968","https://openalex.org/W2963149653","https://openalex.org/W2963546708","https://openalex.org/W3100726593","https://openalex.org/W3118608800","https://openalex.org/W4320013936","https://openalex.org/W6637373629","https://openalex.org/W6675354045","https://openalex.org/W6713134421","https://openalex.org/W6755251542"],"related_works":["https://openalex.org/W2961173803","https://openalex.org/W4367394211","https://openalex.org/W4283819694","https://openalex.org/W2100941997","https://openalex.org/W2327391223","https://openalex.org/W4295141346","https://openalex.org/W2620754580","https://openalex.org/W1541330370","https://openalex.org/W2805008194","https://openalex.org/W2553531723"],"abstract_inverted_index":{"This":[0,72],"paper":[1],"proposes":[2],"a":[3,27,69,91,97,104,129],"method":[4,57,122],"to":[5,74,102],"use":[6],"deep":[7],"neural":[8],"networks":[9],"as":[10],"end-to-end":[11],"open-set":[12,22,40,85],"classifiers.":[13],"It":[14],"is":[15,88,100],"based":[16],"on":[17,111],"intraclass":[18],"data":[19,60],"splitting.":[20],"In":[21,95],"recognition,":[23],"only":[24],"samples":[25,44,51],"from":[26,45,52],"limited":[28],"number":[29],"of":[30,119],"known":[31,53],"classes":[32,47,78],"are":[33],"available":[34],"for":[35],"training.":[36],"During":[37],"inference,":[38],"an":[39],"classifier":[41],"must":[42],"reject":[43],"unknown":[46],"while":[48],"correctly":[49],"classifying":[50],"classes.":[54],"The":[55],"proposed":[56,101,121],"splits":[58],"given":[59],"into":[61,90],"typical":[62],"and":[63,127],"atypical":[64,80],"normal":[65,81],"subsets":[66],"by":[67,79],"using":[68],"closed-set":[70,98,106],"classifier.":[71],"enables":[73],"model":[75],"the":[76,84,117,120,125,133],"abnormal":[77],"samples.":[82],"Accordingly,":[83],"recognition":[86],"problem":[87],"reformulated":[89],"traditional":[92],"classification":[93,107],"problem.":[94],"addition,":[96],"regularization":[99],"guarantee":[103],"high":[105],"performance.":[108],"Intensive":[109],"experiments":[110],"five":[112],"well-known":[113],"image":[114],"datasets":[115],"showed":[116],"effectiveness":[118],"which":[123],"outperformed":[124],"baselines":[126],"achieved":[128],"distinct":[130],"improvement":[131],"over":[132],"state-of-the-art":[134],"methods.":[135]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":7}],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2025-10-10T00:00:00"}