{"id":"https://openalex.org/W3205570990","doi":null,"title":"S2C2 -- An orthogonal method for Semi-Supervised Learning on ambiguous labels","display_name":"S2C2 -- An orthogonal method for Semi-Supervised Learning on ambiguous labels","publication_year":2021,"publication_date":"2021-10-13","ids":{"openalex":"https://openalex.org/W3205570990","mag":"3205570990"},"language":"en","primary_location":{"id":"mag:3205570990","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2106.16209.pdf","pdf_url":null,"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,"listed_in":[],"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":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2106.16209.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5022955518","display_name":"Lars Schmarje","orcid":"https://orcid.org/0000-0002-6945-5957","observed_orcids":["https://orcid.org/0000-0002-6945-5957"]},"institutions":[{"id":"https://openalex.org/I32021983","display_name":"Christian-Albrechts-Universit\u00e4t zu Kiel","ror":"https://ror.org/04v76ef78","country_code":"DE","type":"education","lineage":["https://openalex.org/I32021983"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Lars Schmarje","raw_affiliation_strings":["University of Kiel,"],"raw_orcid":"https://orcid.org/0000-0002-6945-5957","affiliations":[{"raw_affiliation_string":"University of Kiel,","institution_ids":["https://openalex.org/I32021983"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074395199","display_name":"Monty Santarossa","orcid":"https://orcid.org/0000-0002-4159-1367","observed_orcids":["https://orcid.org/0000-0002-4159-1367"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Monty Santarossa","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-4159-1367","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067670733","display_name":"Simon\u2010Martin Schr\u00f6der","orcid":"https://orcid.org/0000-0002-6603-9907","observed_orcids":["https://orcid.org/0000-0002-6603-9907"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Simon-Martin Schr\u00f6der","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-6603-9907","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045272470","display_name":"Claudius Zelenka","orcid":"https://orcid.org/0000-0002-9902-2212","observed_orcids":["https://orcid.org/0000-0002-9902-2212"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Claudius Zelenka","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-9902-2212","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059058911","display_name":"Rainer Kiko","orcid":"https://orcid.org/0000-0002-7851-9107","observed_orcids":["https://orcid.org/0000-0002-7851-9107"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rainer Kiko","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-7851-9107","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033735221","display_name":"Jenny Stracke","orcid":"https://orcid.org/0000-0002-9986-9720","observed_orcids":["https://orcid.org/0000-0002-9986-9720"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jenny Stracke","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-9986-9720","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029510994","display_name":"Nina Volkmann","orcid":"https://orcid.org/0000-0003-2870-9954","observed_orcids":["https://orcid.org/0000-0003-2870-9954"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nina Volkmann","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-2870-9954","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5040944275","display_name":"Reinhard Koch","orcid":"https://orcid.org/0000-0003-4398-1569","observed_orcids":["https://orcid.org/0000-0003-4398-1569"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Reinhard Koch","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-4398-1569","affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.8931000232696533,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.8931000232696533,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.05920000001788139,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.04230000078678131,"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/semi-supervised-learning","display_name":"semi-supervised learning","score":0.9980000257492065},{"id":"https://openalex.org/keywords/ambiguous-labels","display_name":"ambiguous labels","score":0.9909999966621399},{"id":"https://openalex.org/keywords/class-boundaries","display_name":"class boundaries","score":0.4620000123977661},{"id":"https://openalex.org/keywords/intra-observer-variability","display_name":"intraobserver variability","score":0.7689999938011169},{"id":"https://openalex.org/keywords/interobserver-variability","display_name":"interobserver variability","score":0.9919999837875366}],"concepts":[{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.8170809745788574},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.754338264465332},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6592841148376465},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6412844061851501},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.6202160120010376},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5672004818916321},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5010695457458496},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49832940101623535},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.4639992117881775},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3757087290287018},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1683638095855713},{"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3205570990","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2106.16209.pdf","pdf_url":null,"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,"listed_in":[],"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":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:3205570990","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2106.16209.pdf","pdf_url":null,"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,"listed_in":[],"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":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1479807131","https://openalex.org/W1977741415","https://openalex.org/W2005912592","https://openalex.org/W2118858186","https://openalex.org/W2145901919","https://openalex.org/W2163605009","https://openalex.org/W2167363078","https://openalex.org/W2530816535","https://openalex.org/W2626967530","https://openalex.org/W2791302361","https://openalex.org/W2800292742","https://openalex.org/W2807409681","https://openalex.org/W2886281300","https://openalex.org/W2901436880","https://openalex.org/W2913939497","https://openalex.org/W2943865428","https://openalex.org/W2969769592","https://openalex.org/W2978639826","https://openalex.org/W2982445058","https://openalex.org/W2990500698","https://openalex.org/W2992232939","https://openalex.org/W2996060033","https://openalex.org/W2996108195","https://openalex.org/W3001309956","https://openalex.org/W3034363135","https://openalex.org/W3035060554","https://openalex.org/W3035160371","https://openalex.org/W3036224891","https://openalex.org/W3036982689","https://openalex.org/W3039616371","https://openalex.org/W3042609801","https://openalex.org/W3099570996","https://openalex.org/W3118608800","https://openalex.org/W3125745296","https://openalex.org/W3134652006","https://openalex.org/W3136604105","https://openalex.org/W3168547821","https://openalex.org/W3180562345","https://openalex.org/W3204667605","https://openalex.org/W3209934566"],"related_works":["https://openalex.org/W3175354493","https://openalex.org/W4312466177","https://openalex.org/W3209934566","https://openalex.org/W7133450479","https://openalex.org/W2908698261","https://openalex.org/W4312297403","https://openalex.org/W4281569515","https://openalex.org/W4221143433","https://openalex.org/W3106611824","https://openalex.org/W7168204287"],"abstract_inverted_index":{"Semi-Supervised":[0,71,166],"Learning":[1,167],"(SSL)":[2],"can":[3,77,147],"decrease":[4,151],"the":[5,14,55,86,93,106,143,152,157],"required":[6],"amount":[7],"of":[8,45,57,88,111,132,145,154,159,165,176],"labeled":[9,102],"image":[10,90],"data":[11,103,108],"and":[12,59,91,127,140,179],"thus":[13,180],"cost":[15],"for":[16,125],"deep":[17,60,80],"learning.":[18],"Most":[19],"SSL":[20,58,81,95,138],"methods":[21],"assume":[22],"a":[23,98,121,163],"clear":[24],"distinction":[25],"between":[26],"classes,":[27],"but":[28],"class":[29],"boundaries":[30],"are":[31],"often":[32],"ambiguous":[33,107,177],"in":[34,62,120],"real-world":[35,181],"datasets":[36],"due":[37,64],"to":[38,65,100,150,173],"intra-":[39],"or":[40],"interobserver":[41],"variability.":[42],"This":[43],"ambiguity":[44,87,153],"annotations":[46],"must":[47],"be":[48,148],"addressed":[49],"as":[50,97],"it":[51],"will":[52],"otherwise":[53],"limit":[54],"performance":[56],"learning":[61],"general":[63],"inconsistent":[66],"label":[67],"information.":[68],"We":[69,115],"propose":[70],"Classification":[72],"&":[73],"Clustering":[74],"(S2C2)":[75],"which":[76],"extend":[78],"many":[79],"algorithms.":[82],"S2C2":[83,118,146,171],"automatically":[84],"estimates":[85],"an":[89],"applies":[92],"respective":[94],"algorithm":[96],"classification":[99],"certainly":[101],"while":[104],"partitioning":[105],"into":[109],"clusters":[110,133],"visual":[112],"similar":[113],"images.":[114],"show":[116],"that":[117],"results":[119],"7.6%":[122],"better":[123,174],"F1-score":[124],"classifications":[126],"7.9%":[128],"lower":[129],"inner":[130],"distance":[131],"on":[134],"average":[135],"across":[136],"multiple":[137],"algorithms":[139],"datasets.":[141,182],"Moreover,":[142],"output":[144],"used":[149],"labels":[155,178],"with":[156,168],"help":[158],"human":[160],"experts.":[161],"Overall,":[162],"combination":[164],"our":[169],"method":[170],"leads":[172],"handling":[175]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2021-10-25T00:00:00"}