{"id":"https://openalex.org/W4312966868","doi":"https://doi.org/10.1109/icpr56361.2022.9956563","title":"LDGAN: Latent Determined Ensemble Helps Removing IID Data Assumption and Cross-node Sampling in Distributed GANs","display_name":"LDGAN: Latent Determined Ensemble Helps Removing IID Data Assumption and Cross-node Sampling in Distributed GANs","publication_year":2022,"publication_date":"2022-08-21","ids":{"openalex":"https://openalex.org/W4312966868","doi":"https://doi.org/10.1109/icpr56361.2022.9956563"},"language":"en","primary_location":{"id":"doi:10.1109/icpr56361.2022.9956563","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956563","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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/A5059229969","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0001-9596-2752"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wei Wang","raw_affiliation_strings":["Nankai University Tianjin Key Laboratory of Network and Data Security Technology,Trusted AI Laboratory, College of Computer Science,Tianjin,China","Trusted AI Laboratory, College of Computer Science, Nankai University Tianjin Key Laboratory of Network and Data Security Technology, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Nankai University Tianjin Key Laboratory of Network and Data Security Technology,Trusted AI Laboratory, College of Computer Science,Tianjin,China","institution_ids":["https://openalex.org/I205237279"]},{"raw_affiliation_string":"Trusted AI Laboratory, College of Computer Science, Nankai University Tianjin Key Laboratory of Network and Data Security Technology, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089271064","display_name":"Ziwen Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziwen Wu","raw_affiliation_strings":["Nankai University Tianjin Key Laboratory of Network and Data Security Technology,Trusted AI Laboratory, College of Computer Science,Tianjin,China","Trusted AI Laboratory, College of Computer Science, Nankai University Tianjin Key Laboratory of Network and Data Security Technology, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Nankai University Tianjin Key Laboratory of Network and Data Security Technology,Trusted AI Laboratory, College of Computer Science,Tianjin,China","institution_ids":["https://openalex.org/I205237279"]},{"raw_affiliation_string":"Trusted AI Laboratory, College of Computer Science, Nankai University Tianjin Key Laboratory of Network and Data Security Technology, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089966269","display_name":"Xueshuang Xiang","orcid":"https://orcid.org/0000-0001-7794-4876"},"institutions":[{"id":"https://openalex.org/I194716290","display_name":"China Academy of Space Technology","ror":"https://ror.org/025397a59","country_code":"CN","type":"government","lineage":["https://openalex.org/I194716290","https://openalex.org/I2802615301"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xueshuang Xiang","raw_affiliation_strings":["China Academy of Space Technology,Qian Xuesen Laboratory of Space Technology,Beijing,China","Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"China Academy of Space Technology,Qian Xuesen Laboratory of Space Technology,Beijing,China","institution_ids":["https://openalex.org/I194716290"]},{"raw_affiliation_string":"Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing, China","institution_ids":["https://openalex.org/I194716290"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100621538","display_name":"Yue Li","orcid":"https://orcid.org/0000-0001-5482-6244"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Li","raw_affiliation_strings":["Nankai University Tianjin Key Laboratory of Network and Data Security Technology,Trusted AI Laboratory, College of Computer Science,Tianjin,China","Trusted AI Laboratory, College of Computer Science, Nankai University Tianjin Key Laboratory of Network and Data Security Technology, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Nankai University Tianjin Key Laboratory of Network and Data Security Technology,Trusted AI Laboratory, College of Computer Science,Tianjin,China","institution_ids":["https://openalex.org/I205237279"]},{"raw_affiliation_string":"Trusted AI Laboratory, College of Computer Science, Nankai University Tianjin Key Laboratory of Network and Data Security Technology, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5059229969"],"corresponding_institution_ids":["https://openalex.org/I205237279"],"apc_list":null,"apc_paid":null,"fwci":0.1806,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.53571938,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"2135","last_page":"2141"},"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":1.0,"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":1.0,"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/T11448","display_name":"Face recognition and analysis","score":0.9987000226974487,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9955999851226807,"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/mnist-database","display_name":"MNIST database","score":0.8940838575363159},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.7414870262145996},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7208684086799622},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.6364292502403259},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5735398530960083},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.5713667869567871},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.4995136260986328},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.4901317059993744},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4422118663787842},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42602914571762085},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.4239398241043091},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3553621172904968},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3263171315193176},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.1953985095024109},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.08001440763473511}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.8940838575363159},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.7414870262145996},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7208684086799622},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.6364292502403259},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5735398530960083},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.5713667869567871},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.4995136260986328},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.4901317059993744},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4422118663787842},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42602914571762085},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.4239398241043091},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3553621172904968},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3263171315193176},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.1953985095024109},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.08001440763473511},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","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/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr56361.2022.9956563","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956563","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 26th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6899999976158142,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320334978","display_name":"Beijing Nova Program","ror":"https://ror.org/034k14f91"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W2125389028","https://openalex.org/W2593414223","https://openalex.org/W2739748921","https://openalex.org/W2750384547","https://openalex.org/W2808401425","https://openalex.org/W2899730059","https://openalex.org/W2911275792","https://openalex.org/W2945142306","https://openalex.org/W2963226019","https://openalex.org/W2963684088","https://openalex.org/W2991327923","https://openalex.org/W3015598833","https://openalex.org/W3035014113","https://openalex.org/W3083242033","https://openalex.org/W3084487255","https://openalex.org/W3089866310","https://openalex.org/W3109076782","https://openalex.org/W3110899937","https://openalex.org/W3118608800","https://openalex.org/W3185003435","https://openalex.org/W3188271943","https://openalex.org/W3189284366","https://openalex.org/W3193989161","https://openalex.org/W3209482227","https://openalex.org/W3209917595","https://openalex.org/W4205437050","https://openalex.org/W4214519989","https://openalex.org/W4221148876","https://openalex.org/W4221148897","https://openalex.org/W4224943538","https://openalex.org/W4226329309","https://openalex.org/W4287758287","https://openalex.org/W4293320219","https://openalex.org/W4295312788","https://openalex.org/W4295521014","https://openalex.org/W4298289240","https://openalex.org/W4387074584","https://openalex.org/W6631190155","https://openalex.org/W6637568146","https://openalex.org/W6678815747","https://openalex.org/W6685352114","https://openalex.org/W6718140377","https://openalex.org/W6729767818","https://openalex.org/W6735913928","https://openalex.org/W6738560869","https://openalex.org/W6741832134","https://openalex.org/W6743688258","https://openalex.org/W6758673162","https://openalex.org/W6762449936","https://openalex.org/W6766978945","https://openalex.org/W6773685727","https://openalex.org/W6779499942","https://openalex.org/W6786398463","https://openalex.org/W6787972765","https://openalex.org/W6798754374","https://openalex.org/W6800303243","https://openalex.org/W6803011084","https://openalex.org/W6806187798","https://openalex.org/W6809453916","https://openalex.org/W6810380779","https://openalex.org/W6810732526","https://openalex.org/W6810815564"],"related_works":["https://openalex.org/W4293320219","https://openalex.org/W2953246223","https://openalex.org/W3110074278","https://openalex.org/W4283584549","https://openalex.org/W2554314924","https://openalex.org/W4288256692","https://openalex.org/W2998859928","https://openalex.org/W3156863413","https://openalex.org/W4381885966","https://openalex.org/W2986089616"],"abstract_inverted_index":{"Generative":[0,99],"Adversarial":[1,100],"Networks":[2],"(GANs)":[3],"have":[4,20],"received":[5],"a":[6,33,89,103],"lot":[7],"of":[8,45,59,63,113,127,139,143],"attention":[9],"due":[10,37],"to":[11,38,72,76,109],"their":[12],"powerful":[13],"generative":[14],"ability,":[15],"and":[16,133,146,156,159,173,184,186,196],"many":[17,52],"related":[18],"studies":[19],"been":[21],"carried":[22],"out.":[23],"Among":[24],"them,":[25],"deploying":[26],"GANs":[27,50],"in":[28,42,123],"distributed":[29,49],"scenarios":[30],"has":[31],"become":[32],"hot":[34],"research":[35],"topic":[36],"the":[39,43,81,111,114,121,124,137,140,157,170,176,187,200],"sharp":[40],"increase":[41],"capacity":[44],"training":[46,112],"data.":[47],"However,":[48],"face":[51],"challenges,":[53],"such":[54],"as":[55,136],"different":[56],"data":[57,65,128,154,163],"distribution":[58,129,161],"each":[60,131,144],"node,":[61],"limitation":[62],"cross-node":[64,153],"sampling,":[66],"etc.":[67],"The":[68],"previous":[69],"methods":[70],"defaulted":[71],"some":[73],"strong":[74],"assumptions":[75],"alleviate":[77],"these":[78],"problems,":[79],"but":[80],"performance":[82],"would":[83],"be":[84],"degraded":[85],"once":[86],"they":[87],"encountered":[88],"natural":[90],"scene.":[91],"In":[92],"this":[93,135],"paper,":[94],"we":[95],"propose":[96],"Latent":[97],"Determined":[98],"Network":[101],"(LDGAN),":[102],"network":[104],"introducing":[105],"latent":[106,125],"determined":[107],"ensemble":[108],"guide":[110],"generator":[115],"without":[116],"specific":[117],"assumptions.":[118],"LDGAN":[119,180],"measures":[120],"difference":[122],"space":[126],"at":[130],"node":[132],"uses":[134],"weight":[138],"feedback":[141],"information":[142],"discriminator,":[145],"there":[147],"is":[148,193],"no":[149],"need":[150],"for":[151],"any":[152],"sampling":[155],"independent":[158],"identical":[160],"(iid)":[162],"assumption.":[164],"Our":[165],"experiments":[166],"show":[167],"that":[168],"on":[169],"MNIST,":[171],"Fashion-MNIST,":[172],"CIFAR-10":[174],"datasets,":[175],"images":[177],"generated":[178],"by":[179],"are":[181],"more":[182],"realistic":[183],"diverse,":[185],"achieved":[188],"Fr\u00e9chet":[189],"Inception":[190],"Distance":[191],"(FID)":[192],"15.0%,":[194],"22.8%,":[195],"15.3%":[197],"smaller":[198],"than":[199],"state-of-the-art":[201],"models,":[202],"respectively.":[203]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}