{"id":"https://openalex.org/W3037164804","doi":"https://doi.org/10.1109/icpr48806.2021.9412608","title":"Deep Ordinal Regression with Label Diversity","display_name":"Deep Ordinal Regression with Label Diversity","publication_year":2021,"publication_date":"2021-01-10","ids":{"openalex":"https://openalex.org/W3037164804","doi":"https://doi.org/10.1109/icpr48806.2021.9412608","mag":"3037164804"},"language":"en","primary_location":{"id":"doi:10.1109/icpr48806.2021.9412608","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412608","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2006.15864","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Axel Berg","orcid":null},"institutions":[{"id":"https://openalex.org/I1279596006","display_name":"Statistics Sweden","ror":"https://ror.org/05x7wz523","country_code":"SE","type":"government","lineage":["https://openalex.org/I1279596006"]},{"id":"https://openalex.org/I187531555","display_name":"Lund University","ror":"https://ror.org/012a77v79","country_code":"SE","type":"education","lineage":["https://openalex.org/I187531555"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Axel Berg","raw_affiliation_strings":["Center for Mathematical Sciences, Lund University, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Mathematical Sciences, Lund University, Sweden","institution_ids":["https://openalex.org/I1279596006","https://openalex.org/I187531555"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Magnus Oskarsson","orcid":null},"institutions":[{"id":"https://openalex.org/I1279596006","display_name":"Statistics Sweden","ror":"https://ror.org/05x7wz523","country_code":"SE","type":"government","lineage":["https://openalex.org/I1279596006"]},{"id":"https://openalex.org/I187531555","display_name":"Lund University","ror":"https://ror.org/012a77v79","country_code":"SE","type":"education","lineage":["https://openalex.org/I187531555"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Magnus Oskarsson","raw_affiliation_strings":["Center for Mathematical Sciences, Lund University, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Mathematical Sciences, Lund University, Sweden","institution_ids":["https://openalex.org/I1279596006","https://openalex.org/I187531555"]}]},{"author_position":"last","author":{"id":null,"display_name":"Mark O'Connor","orcid":null},"institutions":[{"id":"https://openalex.org/I4210156213","display_name":"American Rock Mechanics Association","ror":"https://ror.org/05vfrxy92","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210156213"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mark O'Connor","raw_affiliation_strings":["Arm Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Arm Research","institution_ids":["https://openalex.org/I4210156213"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.1028,"has_fulltext":false,"cited_by_count":35,"citation_normalized_percentile":{"value":0.95177305,"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":"2740","last_page":"2747"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9980000257492065,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9980000257492065,"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":0.9876000285148621,"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/T10320","display_name":"Neural Networks and Applications","score":0.9866999983787537,"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/artificial-neural-network","display_name":"Artificial neural network","score":0.6378999948501587},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5764999985694885},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4636000096797943},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.43389999866485596},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4318000078201294},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4255000054836273},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.40799999237060547},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.40059998631477356},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.3801000118255615}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6715999841690063},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6378999948501587},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5764999985694885},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4636000096797943},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45739999413490295},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4440999925136566},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.43709999322891235},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.43389999866485596},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4318000078201294},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4255000054836273},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.40799999237060547},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.40059998631477356},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3801000118255615},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3646000027656555},{"id":"https://openalex.org/C110313322","wikidata":"https://www.wikidata.org/wiki/Q7100793","display_name":"Ordinal regression","level":2,"score":0.36039999127388},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.3296000063419342},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.3264000117778778},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31299999356269836},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3118000030517578},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C35519122","wikidata":"https://www.wikidata.org/wiki/Q3775699","display_name":"Segmented regression","level":4,"score":0.302700012922287},{"id":"https://openalex.org/C73000952","wikidata":"https://www.wikidata.org/wiki/Q17007827","display_name":"Discretization","level":2,"score":0.30160000920295715},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.2921000123023987},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C149769383","wikidata":"https://www.wikidata.org/wiki/Q7520804","display_name":"Simple linear regression","level":3,"score":0.2651999890804291},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.26510000228881836},{"id":"https://openalex.org/C74127309","wikidata":"https://www.wikidata.org/wiki/Q3455886","display_name":"Nonparametric regression","level":3,"score":0.2590000033378601},{"id":"https://openalex.org/C85461838","wikidata":"https://www.wikidata.org/wiki/Q7100785","display_name":"Ordinal data","level":2,"score":0.25290000438690186}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icpr48806.2021.9412608","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412608","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2006.15864","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2006.15864","pdf_url":"https://arxiv.org/pdf/2006.15864","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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"},{"id":"pmh:oai:lup.lub.lu.se:2f6e765c-7016-4a5a-98ab-c6c95ae28cf0","is_oa":false,"landing_page_url":"https://lup.lub.lu.se/record/2f6e765c-7016-4a5a-98ab-c6c95ae28cf0","pdf_url":null,"source":{"id":"https://openalex.org/S4306400536","display_name":"Lund University Publications (Lund University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I187531555","host_organization_name":"Lund University","host_organization_lineage":["https://openalex.org/I187531555"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISSN: 1051-4651","raw_type":"info:eu-repo/semantics/conferencePaper"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2006.15864","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2006.15864","pdf_url":"https://arxiv.org/pdf/2006.15864","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[{"id":"https://openalex.org/F4320322327","display_name":"Knut och Alice Wallenbergs Stiftelse","ror":"https://ror.org/004hzzk67"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W114375244","https://openalex.org/W337610345","https://openalex.org/W1899309388","https://openalex.org/W1949483711","https://openalex.org/W2135148528","https://openalex.org/W2149382413","https://openalex.org/W2194775991","https://openalex.org/W2239239723","https://openalex.org/W2244486986","https://openalex.org/W2440214111","https://openalex.org/W2553156677","https://openalex.org/W2592232824","https://openalex.org/W2777551880","https://openalex.org/W2923153064","https://openalex.org/W2949208911","https://openalex.org/W2957744218","https://openalex.org/W2963404738","https://openalex.org/W2963488291","https://openalex.org/W2963644257","https://openalex.org/W2963802733","https://openalex.org/W2964171387","https://openalex.org/W4212883601","https://openalex.org/W6636704036","https://openalex.org/W6676671879","https://openalex.org/W6679285541","https://openalex.org/W6758517951"],"related_works":[],"abstract_inverted_index":{"Regression":[0],"via":[1],"classification":[2],"(RvC)":[3],"is":[4,58,101],"a":[5,21,31,42,52,96,109,140,146],"common":[6],"method":[7,124,133],"used":[8],"for":[9],"regression":[10,54],"problems":[11],"in":[12],"deep":[13,118],"learning,":[14],"where":[15],"the":[16,28,62,74,135],"target":[17,29],"variable":[18],"belongs":[19],"to":[20,50,95,112,139],"set":[22,32,63],"of":[23,33,64],"continuous":[24],"values.":[25],"By":[26],"discretizing":[27],"into":[30],"non-overlapping":[34],"classes,":[35],"it":[36,57,72],"has":[37],"been":[38],"shown":[39],"that":[40,82,131],"training":[41],"classifier":[43],"can":[44,89,105],"improve":[45,90],"neural":[46,91,119],"network":[47,92],"accuracy":[48],"compared":[49,94,138],"using":[51,83],"standard":[53],"approach.":[55],"However,":[56],"not":[59],"clear":[60],"how":[61,71],"discrete":[65,85],"classes":[66],"should":[67],"be":[68,106],"chosen":[69],"and":[70,104,129],"affects":[73],"overall":[75],"solution.":[76],"In":[77],"this":[78],"work,":[79],"we":[80],"propose":[81],"several":[84],"data":[86],"representations":[87],"simultaneously":[88],"learning":[93,114],"single":[97],"representation.":[98],"Our":[99],"approach":[100,143],"end-to-end":[102],"differentiable":[103],"added":[107],"as":[108,117],"simple":[110],"extension":[111],"conventional":[113],"methods,":[115],"such":[116],"networks.":[120],"We":[121],"test":[122],"our":[123,132],"on":[125],"three":[126],"challenging":[127],"tasks":[128],"show":[130],"reduces":[134],"prediction":[136],"error":[137],"baseline":[141],"RvC":[142],"while":[144],"maintaining":[145],"similar":[147],"model":[148],"complexity.":[149]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":4}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2020-07-02T00:00:00"}
