{"id":"https://openalex.org/W3112342379","doi":"https://doi.org/10.1109/ipta50016.2020.9286619","title":"Mixtures of Variational Autoencoders","display_name":"Mixtures of Variational Autoencoders","publication_year":2020,"publication_date":"2020-11-09","ids":{"openalex":"https://openalex.org/W3112342379","doi":"https://doi.org/10.1109/ipta50016.2020.9286619","mag":"3112342379"},"language":"en","primary_location":{"id":"doi:10.1109/ipta50016.2020.9286619","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipta50016.2020.9286619","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://eprints.whiterose.ac.uk/168548/1/IPTA2020a.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100357221","display_name":"Fei Ye","orcid":"https://orcid.org/0000-0002-5894-2178"},"institutions":[{"id":"https://openalex.org/I52099693","display_name":"University of York","ror":"https://ror.org/04m01e293","country_code":"GB","type":"education","lineage":["https://openalex.org/I52099693"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Fei Ye","raw_affiliation_strings":["Department of Computer Science, University of York, York, UK"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of York, York, UK","institution_ids":["https://openalex.org/I52099693"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002932489","display_name":"Adrian G. Bor\u015f","orcid":"https://orcid.org/0000-0001-7838-0021"},"institutions":[{"id":"https://openalex.org/I52099693","display_name":"University of York","ror":"https://ror.org/04m01e293","country_code":"GB","type":"education","lineage":["https://openalex.org/I52099693"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Adrian G. Bors","raw_affiliation_strings":["Department of Computer Science, University of York, York, UK"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of York, York, UK","institution_ids":["https://openalex.org/I52099693"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100357221"],"corresponding_institution_ids":["https://openalex.org/I52099693"],"apc_list":null,"apc_paid":null,"fwci":1.1778,"has_fulltext":true,"cited_by_count":17,"citation_normalized_percentile":{"value":0.81893836,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9997000098228455,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.9865000247955322,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.986299991607666,"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/autoencoder","display_name":"Autoencoder","score":0.8041485548019409},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.6027089357376099},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5274083018302917},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5248234868049622},{"id":"https://openalex.org/keywords/reproducing-kernel-hilbert-space","display_name":"Reproducing kernel Hilbert space","score":0.523574709892273},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.482340544462204},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4784310460090637},{"id":"https://openalex.org/keywords/hilbert-space","display_name":"Hilbert space","score":0.47477957606315613},{"id":"https://openalex.org/keywords/dirichlet-distribution","display_name":"Dirichlet distribution","score":0.46739086508750916},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.45965734124183655},{"id":"https://openalex.org/keywords/independence","display_name":"Independence (probability theory)","score":0.44567230343818665},{"id":"https://openalex.org/keywords/mixing","display_name":"Mixing (physics)","score":0.4454338848590851},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.43651530146598816},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3793601989746094},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.34587883949279785},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.26678740978240967},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1163521409034729},{"id":"https://openalex.org/keywords/boundary-value-problem","display_name":"Boundary value problem","score":0.11591562628746033}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8041485548019409},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.6027089357376099},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5274083018302917},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5248234868049622},{"id":"https://openalex.org/C80884492","wikidata":"https://www.wikidata.org/wiki/Q3345678","display_name":"Reproducing kernel Hilbert space","level":3,"score":0.523574709892273},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.482340544462204},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4784310460090637},{"id":"https://openalex.org/C62799726","wikidata":"https://www.wikidata.org/wiki/Q190056","display_name":"Hilbert space","level":2,"score":0.47477957606315613},{"id":"https://openalex.org/C169214877","wikidata":"https://www.wikidata.org/wiki/Q981016","display_name":"Dirichlet distribution","level":3,"score":0.46739086508750916},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.45965734124183655},{"id":"https://openalex.org/C35651441","wikidata":"https://www.wikidata.org/wiki/Q625303","display_name":"Independence (probability theory)","level":2,"score":0.44567230343818665},{"id":"https://openalex.org/C138777275","wikidata":"https://www.wikidata.org/wiki/Q6884054","display_name":"Mixing (physics)","level":2,"score":0.4454338848590851},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43651530146598816},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3793601989746094},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.34587883949279785},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26678740978240967},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1163521409034729},{"id":"https://openalex.org/C182310444","wikidata":"https://www.wikidata.org/wiki/Q1332643","display_name":"Boundary value problem","level":2,"score":0.11591562628746033},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ipta50016.2020.9286619","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipta50016.2020.9286619","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA)","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.whiterose.ac.uk:168548","is_oa":true,"landing_page_url":"https://orcid.org/0000-0001-7838-0021>","pdf_url":"https://eprints.whiterose.ac.uk/168548/1/IPTA2020a.pdf","source":{"id":"https://openalex.org/S4306400854","display_name":"White Rose Research Online (University of Leeds, The University of Sheffield, University of York)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2800616092","host_organization_name":"White Rose University Consortium","host_organization_lineage":["https://openalex.org/I2800616092"],"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":"NonPeerReviewed"}],"best_oa_location":{"id":"pmh:oai:eprints.whiterose.ac.uk:168548","is_oa":true,"landing_page_url":"https://orcid.org/0000-0001-7838-0021>","pdf_url":"https://eprints.whiterose.ac.uk/168548/1/IPTA2020a.pdf","source":{"id":"https://openalex.org/S4306400854","display_name":"White Rose Research Online (University of Leeds, The University of Sheffield, University of York)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2800616092","host_organization_name":"White Rose University Consortium","host_organization_lineage":["https://openalex.org/I2800616092"],"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":"NonPeerReviewed"},"sustainable_development_goals":[{"score":0.4300000071525574,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G3402744448","display_name":null,"funder_award_id":"Titan XP","funder_id":"https://openalex.org/F4320309480","funder_display_name":"Nvidia"}],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3112342379.pdf","grobid_xml":"https://content.openalex.org/works/W3112342379.grobid-xml"},"referenced_works_count":51,"referenced_works":["https://openalex.org/W1909320841","https://openalex.org/W1999698583","https://openalex.org/W2099471712","https://openalex.org/W2108501770","https://openalex.org/W2112552549","https://openalex.org/W2112796928","https://openalex.org/W2113442785","https://openalex.org/W2120211304","https://openalex.org/W2143956139","https://openalex.org/W2151454335","https://openalex.org/W2172085063","https://openalex.org/W2212660284","https://openalex.org/W2554923929","https://openalex.org/W2556467266","https://openalex.org/W2593729559","https://openalex.org/W2752146117","https://openalex.org/W2753391154","https://openalex.org/W2803766319","https://openalex.org/W2892210823","https://openalex.org/W2949416428","https://openalex.org/W2952745707","https://openalex.org/W2962808998","https://openalex.org/W2962897886","https://openalex.org/W2963021820","https://openalex.org/W2963026013","https://openalex.org/W2963261897","https://openalex.org/W2963373786","https://openalex.org/W2963535485","https://openalex.org/W2963684088","https://openalex.org/W2963865839","https://openalex.org/W2964122153","https://openalex.org/W3118608800","https://openalex.org/W4297779379","https://openalex.org/W4318977982","https://openalex.org/W4320013936","https://openalex.org/W6639732818","https://openalex.org/W6675944832","https://openalex.org/W6676726796","https://openalex.org/W6682350343","https://openalex.org/W6685352114","https://openalex.org/W6688325169","https://openalex.org/W6714590955","https://openalex.org/W6718379498","https://openalex.org/W6730333270","https://openalex.org/W6733322467","https://openalex.org/W6734209382","https://openalex.org/W6744531576","https://openalex.org/W6751715993","https://openalex.org/W6755176163","https://openalex.org/W6780248173","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W2159052453","https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2988134182","https://openalex.org/W1882901045","https://openalex.org/W74847246","https://openalex.org/W2035893370","https://openalex.org/W4255225174","https://openalex.org/W2899106999"],"abstract_inverted_index":{"In":[0,63],"this":[1,67],"paper,":[2],"we":[3,72,146],"develop":[4],"a":[5,27,33,41,47,56,74,106,164],"new":[6],"deep":[7,36],"mixture":[8,22,68,75,92,124],"learning":[9,37],"framework,":[10],"aiming":[11],"to":[12,65,88,97,111,126],"learn":[13,127],"underlying":[14],"complex":[15],"data":[16,44,129,156],"structures.":[17],"Each":[18],"component":[19],"in":[20,86,109,134],"the":[21,80,99,113,116,122,135,139,142],"model":[23,38,145],"is":[24,32,84],"implemented":[25],"using":[26],"Variational":[28],"Autoencoder":[29],"(VAE).":[30],"VAE":[31],"well":[34],"known":[35],"which":[39,83,158],"models":[40],"latent":[42,136],"space":[43],"representation":[45],"on":[46,79],"variational":[48],"manifold.":[49],"The":[50],"mixing":[51],"parameters":[52],"are":[53],"estimated":[54],"from":[55],"Dirichlet":[57],"distribution":[58],"modelled":[59],"by":[60],"each":[61],"encoder.":[62],"order":[64,87,110],"train":[66],"model,":[69],"named":[70],"M-VAE,":[71],"derive":[73],"evidence":[76],"lower":[77],"bound":[78],"sample":[81],"log-likelihood,":[82],"optimized":[85],"jointly":[89],"estimate":[90],"all":[91],"components.":[93],"We":[94],"further":[95],"propose":[96],"use":[98],"d-variables":[100],"Hilbert-Schmidt":[101],"Independence":[102],"Criterion":[103],"(dHSIC)":[104],"as":[105],"regularization":[107],"criterion":[108,120],"enforce":[112],"independence":[114],"among":[115],"encoders'":[117],"distributions.":[118],"This":[119],"encourages":[121],"proposed":[123,143],"components":[125],"different":[128],"distributions":[130],"and":[131],"represent":[132],"them":[133],"space.":[137],"During":[138],"experiments":[140],"with":[141,163],"M-VAE":[144],"observe":[147],"that":[148],"it":[149],"can":[150,159],"be":[151,161],"used":[152],"for":[153],"discovering":[154],"disentangled":[155],"representations":[157],"not":[160],"achieved":[162],"single":[165],"VAE.":[166]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":2}],"updated_date":"2026-04-15T08:11:43.952461","created_date":"2025-10-10T00:00:00"}