{"id":"https://openalex.org/W4402916744","doi":"https://doi.org/10.1109/icip51287.2024.10647557","title":"How to Train Your VAE","display_name":"How to Train Your VAE","publication_year":2024,"publication_date":"2024-09-27","ids":{"openalex":"https://openalex.org/W4402916744","doi":"https://doi.org/10.1109/icip51287.2024.10647557"},"language":"en","primary_location":{"id":"doi:10.1109/icip51287.2024.10647557","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip51287.2024.10647557","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5060459506","display_name":"Mariano Rivera","orcid":"https://orcid.org/0000-0002-3211-2467"},"institutions":[{"id":"https://openalex.org/I124618812","display_name":"Mathematics Research Center","ror":"https://ror.org/02nhmp827","country_code":"MX","type":"facility","lineage":["https://openalex.org/I124618812"]}],"countries":["MX"],"is_corresponding":true,"raw_author_name":"Mariano Rivera","raw_affiliation_strings":["Centro de Investigacion en Matematicas A.C.,Guanajuato,Mexico,Gto. 36120"],"affiliations":[{"raw_affiliation_string":"Centro de Investigacion en Matematicas A.C.,Guanajuato,Mexico,Gto. 36120","institution_ids":["https://openalex.org/I124618812"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5060459506"],"corresponding_institution_ids":["https://openalex.org/I124618812"],"apc_list":null,"apc_paid":null,"fwci":3.2025,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.93437524,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3882","last_page":"3888"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10391","display_name":"Healthcare Policy and Management","score":0.16060000658035278,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10391","display_name":"Healthcare Policy and Management","score":0.16060000658035278,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.649099588394165}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.649099588394165}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip51287.2024.10647557","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip51287.2024.10647557","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W569478347","https://openalex.org/W2100495367","https://openalex.org/W2202109488","https://openalex.org/W2302255633","https://openalex.org/W2339754110","https://openalex.org/W2607662938","https://openalex.org/W2747329762","https://openalex.org/W2765811365","https://openalex.org/W2910135751","https://openalex.org/W2913002991","https://openalex.org/W2948978827","https://openalex.org/W2963073614","https://openalex.org/W2970014727","https://openalex.org/W2971074500","https://openalex.org/W2988407809","https://openalex.org/W3176855311","https://openalex.org/W4292382489","https://openalex.org/W6610566761","https://openalex.org/W6639732818","https://openalex.org/W6640963894","https://openalex.org/W6744627333","https://openalex.org/W6745535286","https://openalex.org/W6745560452","https://openalex.org/W6758151150","https://openalex.org/W6762931180","https://openalex.org/W6791597301","https://openalex.org/W6797902607"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"Variational":[0],"Autoencoders":[1],"(VAEs)":[2],"have":[3],"become":[4],"a":[5,19,31,60,63,83,92,101,129],"cornerstone":[6],"in":[7],"generative":[8,135],"modeling":[9],"and":[10,47,59,99,117],"representation":[11],"learning":[12],"within":[13,34],"machine":[14],"learning.":[15],"This":[16],"paper":[17],"explores":[18],"nuanced":[20],"aspect":[21],"of":[22,85],"VAEs,":[23],"focusing":[24],"on":[25,65],"interpreting":[26],"the":[27,35,42,50,66,80,88,115,122],"Kullback-Leibler":[28],"(KL)":[29],"Divergence,":[30],"critical":[32],"component":[33],"Evidence":[36],"Lower":[37],"Bound":[38],"(ELBO)":[39],"that":[40],"governs":[41],"trade-off":[43],"between":[44,55],"reconstruction":[45],"accuracy":[46],"regularization.":[48],"Meanwhile,":[49],"KL":[51],"Divergence":[52],"enforces":[53],"alignment":[54],"latent":[56,68],"variable":[57,73],"distributions":[58,74],"prior":[61],"imposing":[62],"structure":[64],"overall":[67],"space":[69],"but":[70],"leaves":[71],"individual":[72],"unconstrained.":[75],"The":[76,119],"proposed":[77],"method":[78],"redefines":[79],"ELBO":[81],"with":[82],"mixture":[84],"Gaussians":[86],"for":[87,113,132],"posterior":[89],"probability,":[90],"introduces":[91],"regularization":[93],"term":[94],"to":[95,104,124],"prevent":[96],"variance":[97],"collapse,":[98],"employs":[100],"PatchGAN":[102],"discriminator":[103],"enhance":[105],"texture":[106],"realism.":[107],"Implementation":[108],"details":[109],"involve":[110],"ResNetV2":[111],"architectures":[112],"both":[114],"Encoder":[116],"Decoder.":[118],"experiments":[120],"demonstrate":[121],"ability":[123],"generate":[125],"realistic":[126],"faces,":[127],"offering":[128],"promising":[130],"solution":[131],"enhancing":[133],"VAE-based":[134],"models.":[136]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}