{"id":"https://openalex.org/W3012620966","doi":"https://doi.org/10.1145/3366423.3380293","title":"Estimate the Implicit Likelihoods of GANs with Application to Anomaly Detection","display_name":"Estimate the Implicit Likelihoods of GANs with Application to Anomaly Detection","publication_year":2020,"publication_date":"2020-04-20","ids":{"openalex":"https://openalex.org/W3012620966","doi":"https://doi.org/10.1145/3366423.3380293","mag":"3012620966"},"language":"en","primary_location":{"id":"doi:10.1145/3366423.3380293","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380293","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3366423.3380293","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5063255678","display_name":"Shaogang Ren","orcid":"https://orcid.org/0000-0002-2961-1636"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Shaogang Ren","raw_affiliation_strings":["Baidu Research USA"],"affiliations":[{"raw_affiliation_string":"Baidu Research USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103234379","display_name":"Dingcheng Li","orcid":"https://orcid.org/0000-0002-1630-4338"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dingcheng Li","raw_affiliation_strings":["Baidu Research USA"],"affiliations":[{"raw_affiliation_string":"Baidu Research USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101854736","display_name":"Zhixin Zhou","orcid":"https://orcid.org/0000-0003-3737-9248"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhixin Zhou","raw_affiliation_strings":["Baidu Research USA"],"affiliations":[{"raw_affiliation_string":"Baidu Research USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100435494","display_name":"Ping Li","orcid":"https://orcid.org/0000-0002-1503-0240"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ping Li","raw_affiliation_strings":["Baidu Research USA"],"affiliations":[{"raw_affiliation_string":"Baidu Research USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5063255678"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0605,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.81603549,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2287","last_page":"2297"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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":1.0,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9855999946594238,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9732999801635742,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/computer-science","display_name":"Computer science","score":0.7022762298583984},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.644014835357666},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.5972062349319458},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.5003952980041504},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4952453672885895},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.48311305046081543},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.4796331524848938},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4254875183105469},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40453779697418213},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40307411551475525},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3888136148452759},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.08074849843978882}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7022762298583984},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.644014835357666},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.5972062349319458},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5003952980041504},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4952453672885895},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.48311305046081543},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4796331524848938},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4254875183105469},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40453779697418213},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40307411551475525},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3888136148452759},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.08074849843978882},{"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/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"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/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3366423.3380293","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380293","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3366423.3380293","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380293","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1866230956","https://openalex.org/W1959608418","https://openalex.org/W2096484739","https://openalex.org/W2101234009","https://openalex.org/W2105497548","https://openalex.org/W2112796928","https://openalex.org/W2296719434","https://openalex.org/W2398119937","https://openalex.org/W2599354622","https://openalex.org/W2605800822","https://openalex.org/W2743138268","https://openalex.org/W2747329762","https://openalex.org/W2768252793","https://openalex.org/W2786088545","https://openalex.org/W2807036696","https://openalex.org/W2810203579","https://openalex.org/W2902758299","https://openalex.org/W2919115771","https://openalex.org/W2962760235","https://openalex.org/W2962793481","https://openalex.org/W2963045681","https://openalex.org/W2963134136","https://openalex.org/W2963139417","https://openalex.org/W2963142510","https://openalex.org/W2963226019","https://openalex.org/W2963641970","https://openalex.org/W2963919714","https://openalex.org/W2964011399","https://openalex.org/W2978329087","https://openalex.org/W2990138404","https://openalex.org/W3014018559","https://openalex.org/W3118608800","https://openalex.org/W3120740533","https://openalex.org/W3153872861","https://openalex.org/W4247950230","https://openalex.org/W6675354045"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W3210364259","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W4365211920","https://openalex.org/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W4300558037","https://openalex.org/W4377864969"],"abstract_inverted_index":{"The":[0,69,108],"thriving":[1],"of":[2,46,53,62,66,72],"deep":[3],"models":[4,7],"and":[5,23,89],"generative":[6],"provides":[8],"approaches":[9],"to":[10,41,115],"model":[11],"high":[12],"dimensional":[13],"distributions.":[14],"Generative":[15],"adversarial":[16],"networks":[17],"(GANs)":[18],"can":[19,56,76,123],"approximate":[20],"data":[21,25,30,94,131],"distributions":[22],"generate":[24],"samples":[26],"from":[27],"the":[28,43,54,60,67,73,80,84,97,121],"learned":[29,58],"manifolds":[31],"as":[32],"well.":[33],"In":[34],"this":[35],"paper,":[36],"we":[37],"propose":[38],"an":[39],"approach":[40],"estimate":[42],"implicit":[44],"likelihoods":[45],"GAN":[47],"models.":[48],"A":[49],"stable":[50],"inverse":[51],"function":[52],"generator":[55],"be":[57,77],"with":[59],"help":[61],"a":[63],"variance":[64,71],"network":[65],"generator.":[68],"local":[70],"sample":[74],"distribution":[75],"approximated":[78],"by":[79],"normalized":[81],"distance":[82],"in":[83,105],"latent":[85],"space.":[86],"Simulation":[87],"studies":[88],"likelihood":[90],"testing":[91],"on":[92,129],"real-world":[93,130],"sets":[95],"validate":[96],"proposed":[98,109],"algorithm,":[99],"which":[100],"outperforms":[101],"several":[102],"baseline":[103],"methods":[104],"these":[106],"tasks.":[107],"method":[110,122],"has":[111],"been":[112],"further":[113],"applied":[114],"anomaly":[116,126],"detection.":[117],"Experiments":[118],"show":[119],"that":[120],"achieve":[124],"state-of-the-art":[125],"detection":[127],"performance":[128],"sets.":[132]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}