{"id":"https://openalex.org/W4225809763","doi":"https://doi.org/10.48550/arxiv.2204.02010","title":"LatentGAN Autoencoder: Learning Disentangled Latent Distribution","display_name":"LatentGAN Autoencoder: Learning Disentangled Latent Distribution","publication_year":2022,"publication_date":"2022-04-05","ids":{"openalex":"https://openalex.org/W4225809763","doi":"https://doi.org/10.48550/arxiv.2204.02010"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2204.02010","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2204.02010","pdf_url":"https://arxiv.org/pdf/2204.02010","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2204.02010","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5028739639","display_name":"Sanket Kalwar","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Kalwar, Sanket","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053804905","display_name":"Animikh Aich","orcid":"https://orcid.org/0000-0002-0309-7798"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aich, Animikh","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073080117","display_name":"Tanay Dixit","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dixit, Tanay","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Chhabra, Adit","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chhabra, Adit","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5028739639"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9986000061035156,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9986000061035156,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9922000169754028,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9915000200271606,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.9728395938873291},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.9409714341163635},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6768337488174438},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.6319437026977539},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5999230742454529},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5632789731025696},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5135886669158936},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.4995133876800537},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.47406071424484253},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42388302087783813},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3377097249031067},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2446117401123047},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.09335169196128845},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.0535123348236084}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.9728395938873291},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.9409714341163635},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6768337488174438},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.6319437026977539},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5999230742454529},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5632789731025696},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5135886669158936},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.4995133876800537},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.47406071424484253},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42388302087783813},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3377097249031067},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2446117401123047},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.09335169196128845},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0535123348236084},{"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:2204.02010","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2204.02010","pdf_url":"https://arxiv.org/pdf/2204.02010","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"raw_type":"text"},{"id":"pmh:oai:zenodo.org:14699776","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arXiv.2204.02010","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/workingPaper"},{"id":"doi:10.48550/arxiv.2204.02010","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2204.02010","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2204.02010","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2204.02010","pdf_url":"https://arxiv.org/pdf/2204.02010","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4394785709","https://openalex.org/W4296978181","https://openalex.org/W2912987408","https://openalex.org/W2937381246","https://openalex.org/W3004801820","https://openalex.org/W4281672036","https://openalex.org/W4313444753","https://openalex.org/W4230582276","https://openalex.org/W2973021803","https://openalex.org/W4288257096"],"abstract_inverted_index":{"In":[0],"autoencoder,":[1],"the":[2,6,10,13,28,33,53,61,65],"encoder":[3],"generally":[4],"approximates":[5],"latent":[7,20,29,35,62,90],"distribution":[8,63],"over":[9,27],"dataset,":[11],"and":[12,67,75,115],"decoder":[14],"generates":[15],"samples":[16],"using":[17,32,52],"this":[18,49],"learned":[19],"distribution.":[21,91],"There":[22],"is":[23,82,109],"very":[24],"little":[25],"control":[26,88],"vector":[30,36],"as":[31,111],"random":[34],"for":[37],"generation":[38],"will":[39],"lead":[40],"to":[41,47,56,59,87,113],"trivial":[42],"outputs.":[43],"This":[44],"work":[45],"tries":[46],"address":[48],"issue":[50],"by":[51],"LatentGAN":[54],"generator":[55],"directly":[57],"learn":[58],"approximate":[60],"of":[64,101],"autoencoder":[66,89],"show":[68],"meaningful":[69],"results":[70],"on":[71,103],"MNIST,":[72],"3D":[73],"Chair,":[74],"CelebA":[76],"datasets,":[77],"an":[78,98],"additional":[79],"information-theoretic":[80],"constrain":[81],"used":[83],"which":[84,108],"successfully":[85],"learns":[86],"With":[92],"this,":[93],"our":[94],"model":[95],"also":[96],"achieves":[97],"error":[99],"rate":[100],"2.38":[102],"MNIST":[104],"unsupervised":[105],"image":[106],"classification,":[107],"better":[110],"compared":[112],"InfoGAN":[114],"AAE.":[116]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
