{"id":"https://openalex.org/W4281790438","doi":"https://doi.org/10.1145/3529466.3529475","title":"Mixture Density Hyperspherical Generative Adversarial Networks","display_name":"Mixture Density Hyperspherical Generative Adversarial Networks","publication_year":2022,"publication_date":"2022-03-04","ids":{"openalex":"https://openalex.org/W4281790438","doi":"https://doi.org/10.1145/3529466.3529475"},"language":"en","primary_location":{"id":"doi:10.1145/3529466.3529475","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3529466.3529475","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 the 6th International Conference on Innovation in Artificial Intelligence (ICIAI)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5055420303","display_name":"Qinyang Li","orcid":null},"institutions":[{"id":"https://openalex.org/I119045251","display_name":"Huaqiao University","ror":"https://ror.org/03frdh605","country_code":"CN","type":"education","lineage":["https://openalex.org/I119045251"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinyang Li","raw_affiliation_strings":["Huaqiao University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huaqiao University, China","institution_ids":["https://openalex.org/I119045251"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056455910","display_name":"Wentao Fan","orcid":"https://orcid.org/0000-0001-6694-7289"},"institutions":[{"id":"https://openalex.org/I119045251","display_name":"Huaqiao University","ror":"https://ror.org/03frdh605","country_code":"CN","type":"education","lineage":["https://openalex.org/I119045251"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wentao Fan","raw_affiliation_strings":["Huaqiao University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huaqiao University, China","institution_ids":["https://openalex.org/I119045251"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I119045251"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"31","last_page":"37"},"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":0.9995999932289124,"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":0.9995999932289124,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9939000010490417,"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/T11309","display_name":"Music and Audio Processing","score":0.9664000272750854,"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/hypersphere","display_name":"Hypersphere","score":0.9242954254150391},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.8188716173171997},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.8099769949913025},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.6934670805931091},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.654310405254364},{"id":"https://openalex.org/keywords/mode","display_name":"Mode (computer interface)","score":0.5403777360916138},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5145696997642517},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5051923394203186},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.48069339990615845},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.43247953057289124},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.41237786412239075},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.40757447481155396},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34128338098526},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.16759547591209412},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07196033000946045}],"concepts":[{"id":"https://openalex.org/C2776562905","wikidata":"https://www.wikidata.org/wiki/Q306610","display_name":"Hypersphere","level":2,"score":0.9242954254150391},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8188716173171997},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.8099769949913025},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.6934670805931091},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.654310405254364},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.5403777360916138},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5145696997642517},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5051923394203186},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.48069339990615845},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.43247953057289124},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41237786412239075},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.40757447481155396},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34128338098526},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.16759547591209412},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07196033000946045},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3529466.3529475","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3529466.3529475","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 the 6th International Conference on Innovation in Artificial Intelligence (ICIAI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.6800000071525574}],"awards":[{"id":"https://openalex.org/G3980225430","display_name":null,"funder_award_id":"61876068","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W114923250","https://openalex.org/W1834627138","https://openalex.org/W1909320841","https://openalex.org/W2016155919","https://openalex.org/W2145001205","https://openalex.org/W2202109488","https://openalex.org/W2259643685","https://openalex.org/W2577946330","https://openalex.org/W2581485081","https://openalex.org/W2617539464","https://openalex.org/W2739748921","https://openalex.org/W2888960966","https://openalex.org/W2898625866","https://openalex.org/W2963981733","https://openalex.org/W3013267890","https://openalex.org/W3039877981","https://openalex.org/W3047182391","https://openalex.org/W4289256549","https://openalex.org/W6604218104","https://openalex.org/W6640963894","https://openalex.org/W6714590955","https://openalex.org/W6718379498","https://openalex.org/W6729767818","https://openalex.org/W6731412260","https://openalex.org/W6735913928","https://openalex.org/W6738546315","https://openalex.org/W6746090280","https://openalex.org/W6746775625","https://openalex.org/W6749820799"],"related_works":["https://openalex.org/W2099702253","https://openalex.org/W2059484267","https://openalex.org/W4309969736","https://openalex.org/W4394785709","https://openalex.org/W2770818364","https://openalex.org/W2953501176","https://openalex.org/W2965095304","https://openalex.org/W2470043383","https://openalex.org/W3034474024","https://openalex.org/W4317242571"],"abstract_inverted_index":{"The":[0],"Generative":[1],"Adversarial":[2],"Networks":[3],"(GANs)":[4],"are":[5,16],"deep":[6],"generative":[7,80,90,98,156],"models":[8,99],"that":[9,84,100,202],"can":[10,143],"generate":[11,209],"realistic":[12],"samples,":[13],"but":[14],"they":[15],"difficult":[17],"to":[18,23,62,131,153,171,176,208],"train":[19],"in":[20,37,168],"practice":[21],"due":[22],"the":[24,30,40,53,57,68,96,107,125,138,159,169,181,188,194],"problem":[25],"of":[26,49,95,127,140,158,180],"mode":[27,36,178],"collapse,":[28],"where":[29],"generator":[31,69,139],"only":[32,44],"repeatedly":[33],"generates":[34,45],"one":[35],"samples":[38],"during":[39,56],"learning":[41],"process,":[42],"or":[43],"a":[46,76,102,114,133,148,162,173,205],"small":[47],"number":[48],"modes":[50],"after":[51],"reaching":[52],"Nash":[54],"equilibrium":[55],"adversarial":[58,91],"training.":[59],"In":[60,183],"order":[61],"solve":[63],"this":[64],"issue":[65],"while":[66],"making":[67],"contains":[70],"promising":[71],"generation":[72],"ability,":[73],"we":[74,200],"propose":[75],"mixture":[77,164],"density":[78],"hyperspherical":[79,149],"model":[81,118,142,165],"namely":[82],"MDH-GAN":[83,105,203],"combines":[85,119],"variational":[86],"autoencoder":[87],"(VAE)":[88],"and":[89,197],"network.":[92],"Unlike":[93],"most":[94],"GAN-based":[97],"consider":[101],"Gaussian":[103],"prior,":[104],"adopts":[106],"von":[108],"Mises-Fisher":[109],"(vMF)":[110],"prior":[111],"defined":[112],"on":[113],"unit":[115],"hypersphere.":[116],"Our":[117],"VAE":[120,128],"with":[121,129,147,212],"GAN":[122,130],"by":[123,186],"integrating":[124],"encoder":[126],"form":[132,172],"jointly":[134],"training":[135],"framework.":[136],"Therefore,":[137],"our":[141,184],"learn":[144],"data":[145],"distribution":[146],"latent":[150],"structure,":[151],"leading":[152],"an":[154],"improved":[155],"ability":[157,207],"generator.":[160],"Moreover,":[161],"vMF":[163],"is":[166],"deployed":[167],"discriminator":[170],"hypersphere":[174],"space":[175],"avoid":[177],"collapse":[179],"model.":[182],"experiments,":[185],"calculating":[187],"Fr\u00e9chet":[189],"Inception":[190],"distance":[191],"(FID)":[192],"between":[193],"generated":[195],"images":[196,211],"real":[198],"ones,":[199],"prove":[201],"has":[204],"better":[206],"high-quality":[210],"high":[213],"diversity.":[214]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}