{"id":"https://openalex.org/W7133688725","doi":"https://doi.org/10.48550/arxiv.2603.04223","title":"Semi-Supervised Generative Learning via Latent Space Distribution Matching","display_name":"Semi-Supervised Generative Learning via Latent Space Distribution Matching","publication_year":2026,"publication_date":"2026-03-04","ids":{"openalex":"https://openalex.org/W7133688725","doi":"https://doi.org/10.48550/arxiv.2603.04223"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.04223","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"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":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092671686","display_name":"Kwong Yu Chong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chong, Kwong Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128184818","display_name":"Long Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Long","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.25560181,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9591000080108643,"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":0.9591000080108643,"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":0.01360000018030405,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.009399999864399433,"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/matching","display_name":"Matching (statistics)","score":0.6478000283241272},{"id":"https://openalex.org/keywords/joint-probability-distribution","display_name":"Joint probability distribution","score":0.5637999773025513},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.520799994468689},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48420000076293945},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4611999988555908},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.4350999891757965},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.4219000041484833},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.4115000069141388},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.4097999930381775}],"concepts":[{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6478000283241272},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5695000290870667},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.5637999773025513},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.520799994468689},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.49470001459121704},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48420000076293945},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4611999988555908},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4562000036239624},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4350999891757965},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.4219000041484833},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.4115000069141388},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.4097999930381775},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3749000132083893},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.34439998865127563},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.32710000872612},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.320499986410141},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.31060001254081726},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29010000824928284},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.2799000144004822},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27309998869895935},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.26100000739097595},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.25690001249313354}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.04223","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"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":"Article"},{"id":"doi:10.48550/arxiv.2603.04223","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.04223","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2603.04223","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"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":"Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0],"introduce":[1],"Latent":[2,122],"Space":[3],"Distribution":[4],"Matching":[5],"(LSDM),":[6],"a":[7,24,84,108,115,130],"novel":[8],"framework":[9],"for":[10],"semi-supervised":[11],"generative":[12],"modeling":[13],"of":[14,87,101,118,132,155,172],"conditional":[15],"distributions.":[16],"LSDM":[17,106,173],"operates":[18],"in":[19,40,93,134,174],"two":[20,103],"stages:":[21],"(i)":[22],"learning":[23],"low-dimensional":[25],"latent":[26],"space":[27,42],"from":[28],"both":[29],"paired":[30,49,69],"and":[31,34,82,166],"unpaired":[32,88,176],"data,":[33],"(ii)":[35],"performing":[36],"joint":[37,63,136],"distribution":[38,137],"matching":[39,138],"this":[41],"via":[43,142],"the":[44,59,99,153,170],"1-Wasserstein":[45,60],"distance,":[46],"using":[47],"only":[48],"data.":[50],"This":[51],"two-step":[52],"approach":[53],"minimizes":[54],"an":[55],"upper":[56],"bound":[57],"on":[58,67,159],"distance":[61],"between":[62],"distributions,":[64],"reducing":[65],"reliance":[66],"scarce":[68],"samples":[70],"while":[71],"enabling":[72],"fast":[73],"one-step":[74],"generation.":[75],"Theoretically,":[76],"we":[77],"establish":[78],"non-asymptotic":[79],"error":[80],"bounds":[81],"demonstrate":[83,169],"key":[85],"benefit":[86],"data:":[89],"enhanced":[90],"geometric":[91],"fidelity":[92],"generated":[94],"outputs.":[95],"Furthermore,":[96],"by":[97],"extending":[98],"scope":[100],"its":[102],"core":[104],"steps,":[105],"provides":[107],"coherent":[109],"statistical":[110],"perspective":[111],"that":[112],"connects":[113],"to":[114,178],"broad":[116],"class":[117],"latent-space":[119],"approaches.":[120],"Notably,":[121],"Diffusion":[123],"Models":[124],"(LDMs)":[125],"can":[126],"be":[127],"viewed":[128],"as":[129],"variant":[131],"LSDM,":[133],"which":[135],"is":[139],"achieved":[140],"indirectly":[141],"score":[143],"matching.":[144],"Consequently,":[145],"our":[146],"results":[147],"also":[148],"provide":[149],"theoretical":[150],"insights":[151],"into":[152],"consistency":[154],"LDMs.":[156],"Empirical":[157],"evaluations":[158],"real-world":[160],"image":[161,167],"tasks,":[162],"including":[163],"class-conditional":[164],"generation":[165,180],"super-resolution,":[168],"effectiveness":[171],"leveraging":[175],"data":[177],"enhance":[179],"quality.":[181]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-06T00:00:00"}