{"id":"https://openalex.org/W3208785339","doi":"https://doi.org/10.1145/3459637.3482474","title":"ScarceGAN","display_name":"ScarceGAN","publication_year":2021,"publication_date":"2021-10-26","ids":{"openalex":"https://openalex.org/W3208785339","doi":"https://doi.org/10.1145/3459637.3482474","mag":"3208785339"},"language":"en","primary_location":{"id":"doi:10.1145/3459637.3482474","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459637.3482474","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2505.03811","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052532406","display_name":"Surajit Chakrabarty","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Surajit Chakrabarty","raw_affiliation_strings":["Games24x7, Bengaluru, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Games24x7, Bengaluru, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069986100","display_name":"Rukma Talwadker","orcid":"https://orcid.org/0000-0002-1551-9679"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rukma Talwadker","raw_affiliation_strings":["Games24x7.com, Bengaluru, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Games24x7.com, Bengaluru, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063253011","display_name":"Tridib Mukherjee","orcid":"https://orcid.org/0009-0009-2385-1290"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tridib Mukherjee","raw_affiliation_strings":["Games24x7, Bengaluru, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Games24x7, Bengaluru, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5052532406"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4198,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.7031687,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9988999962806702,"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":0.9894999861717224,"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/discriminator","display_name":"Discriminator","score":0.744953453540802},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.608540415763855},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5898847579956055},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.555302619934082},{"id":"https://openalex.org/keywords/jump","display_name":"Jump","score":0.5448330640792847},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5135726928710938},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.4877853989601135},{"id":"https://openalex.org/keywords/skew","display_name":"Skew","score":0.4672052264213562},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.42263495922088623},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3820119798183441},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37630680203437805},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.29598334431648254}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.744953453540802},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.608540415763855},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5898847579956055},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.555302619934082},{"id":"https://openalex.org/C2780695682","wikidata":"https://www.wikidata.org/wiki/Q4005959","display_name":"Jump","level":2,"score":0.5448330640792847},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5135726928710938},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.4877853989601135},{"id":"https://openalex.org/C43711488","wikidata":"https://www.wikidata.org/wiki/Q7534783","display_name":"Skew","level":2,"score":0.4672052264213562},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42263495922088623},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3820119798183441},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37630680203437805},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29598334431648254},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3459637.3482474","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459637.3482474","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2505.03811","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2505.03811","pdf_url":"https://arxiv.org/pdf/2505.03811","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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:2505.03811","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2505.03811","pdf_url":"https://arxiv.org/pdf/2505.03811","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":[{"score":0.7099999785423279,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W2006487144","https://openalex.org/W2025768430","https://openalex.org/W2092295723","https://openalex.org/W2097482982","https://openalex.org/W2100495367","https://openalex.org/W2109316012","https://openalex.org/W2118978333","https://openalex.org/W2132820034","https://openalex.org/W2132870739","https://openalex.org/W2133227149","https://openalex.org/W2135090671","https://openalex.org/W2172292676","https://openalex.org/W2412510955","https://openalex.org/W2593576259","https://openalex.org/W2619371851","https://openalex.org/W2743138268","https://openalex.org/W2747599906","https://openalex.org/W2752432857","https://openalex.org/W2754051771","https://openalex.org/W2796067775","https://openalex.org/W2902758299","https://openalex.org/W2913691358","https://openalex.org/W2949416428","https://openalex.org/W2950204666","https://openalex.org/W2963373786","https://openalex.org/W2969883848","https://openalex.org/W2980539895","https://openalex.org/W2998540609","https://openalex.org/W3032026348","https://openalex.org/W3095353058","https://openalex.org/W3100068262","https://openalex.org/W3133252983"],"related_works":["https://openalex.org/W3110074278","https://openalex.org/W2953246223","https://openalex.org/W4293320219","https://openalex.org/W4283584549","https://openalex.org/W2618858825","https://openalex.org/W2554314924","https://openalex.org/W2998859928","https://openalex.org/W3151498616","https://openalex.org/W4381885966","https://openalex.org/W4288256692"],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"ScarceGAN":[3,137,235,277],"which":[4],"focuses":[5],"on":[6,76,112,160,222],"identification":[7,253,290],"of":[8,52,84,110,123,184,195,213,262,281],"extremely":[9,46],"rare":[10,263,288],"or":[11,86],"scarce":[12,224],"samples":[13,148,166,173],"from":[14,35,271],"multi-dimensional":[15],"longitudinal":[16,292],"telemetry":[17,293],"data":[18,69],"with":[19,56,174,226],"small":[20],"and":[21,60,65,73,79,82,149,188,254],"weak":[22,74],"label":[23],"prior.":[24,176],"We":[25,177,275],"specifically":[26],"address:":[27],"(i)":[28],"severe":[29],"scarcity":[30],"in":[31,40,89,94,130,186,202,207,230,259,267,286],"positive":[32,78,127,152,250],"class,":[33],"stemming":[34],"both":[36,77],"underlying":[37],"organic":[38],"skew":[39],"the":[41,53,90,95,113,121,126,150,156,181,196,231,238,249,268,272,287],"data,":[42],"as":[43,45,191,193],"well":[44,192],"limited":[47],"labels;":[48],"(ii)":[49],"multi-class":[50],"nature":[51],"negative":[54,80,96,114,147,165],"samples,":[55],"uneven":[57],"density":[58],"distributions":[59],"partially":[61],"overlapping":[62],"feature":[63],"distributions;":[64],"(iii)":[66],"massively":[67],"unlabelled":[68,91],"leading":[70],"to":[71,100,119,180,278],"tiny":[72],"prior":[75],"classes,":[81],"possibility":[83],"unseen":[85],"unknown":[87,232],"behavior":[88],"set,":[92],"especially":[93],"class.":[97],"Although":[98],"related":[99],"PU":[101],"learning":[102],"problems,":[103],"we":[104],"contend":[105],"that":[106,194],"knowledge":[107],"(or":[108],"lack":[109],"it)":[111],"class":[115,146,225,252,289],"can":[116],"be":[117,279],"leveraged":[118],"learn":[120],"compliment":[122],"it":[124],"(i.e.,":[125],"class)":[128],"better":[129],"a":[131,169,211,256],"semi-supervised":[132,139,220],"manner.":[133],"To":[134],"this":[135,205],"effect,":[136],"re-formulates":[138],"GAN":[140,244],"by":[141,167,242],"accommodating":[142],"weakly":[143],"labelled":[144],"multi-":[145],"available":[151],"samples.":[153],"It":[154],"relaxes":[155],"supervised":[157,187],"discriminator's":[158],"constraint":[159],"exact":[161],"differentiation":[162],"be-":[163],"tween":[164],"introducing":[168],"'leeway'":[170],"term":[171],"for":[172,248,291],"noisy":[175],"propose":[178],"modifications":[179],"cost":[182],"objectives":[183],"discriminator,":[185],"unsupervised":[189],"path":[190],"generator.":[197],"For":[198],"identifying":[199,260],"risky":[200],"players":[201],"skill":[203],"gaming,":[204],"formulation":[206],"whole":[208],"gives":[209],"us":[210],"recall":[212,239],"over":[214,218],"85%":[215],"(~60%":[216],"jump":[217],"vanilla":[219],"GAN)":[221],"our":[223],"very":[227],"minimal":[228],"verbosity":[229],"space.":[233],"Further":[234],"out-":[236],"performs":[237],"benchmarks":[240],"established":[241],"recent":[243],"based":[245],"specialized":[246],"models":[247],"imbalanced":[251],"establishes":[255],"new":[257,282],"benchmark":[258,284],"one":[261,280],"attack":[264],"classes":[265],"(0.09%)":[266],"intrusion":[269],"dataset":[270],"KDDCUP99":[273],"challenge.":[274],"establish":[276],"competitive":[283],"frameworks":[285],"data.":[294]},"counts_by_year":[{"year":2024,"cited_by_count":3}],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2021-11-08T00:00:00"}