{"id":"https://openalex.org/W4399252635","doi":"https://doi.org/10.1145/3770854.3780252","title":"Exploring Diffusion Models' Corruption Stage in Few-Shot Fine-tuning and Mitigating with Bayesian Neural Networks","display_name":"Exploring Diffusion Models' Corruption Stage in Few-Shot Fine-tuning and Mitigating with Bayesian Neural Networks","publication_year":2026,"publication_date":"2026-04-20","ids":{"openalex":"https://openalex.org/W4399252635","doi":"https://doi.org/10.1145/3770854.3780252"},"language":"en","primary_location":{"id":"doi:10.1145/3770854.3780252","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3770854.3780252","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3770854.3780252","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084627119","display_name":"Xiaoyu Wu","orcid":"https://orcid.org/0000-0001-8456-1160"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyu Wu","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0006-7129-6859","affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059144000","display_name":"Jiaru Zhang","orcid":"https://orcid.org/0000-0001-5909-1005"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaru Zhang","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-9273-9093","affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016123060","display_name":"Hua Yang","orcid":"https://orcid.org/0000-0001-5536-503X"},"institutions":[{"id":"https://openalex.org/I126231945","display_name":"Queen's University Belfast","ror":"https://ror.org/00hswnk62","country_code":"GB","type":"education","lineage":["https://openalex.org/I126231945"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yang Hua","raw_affiliation_strings":["Queen's University Belfast, Belfast, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0001-5536-503X","affiliations":[{"raw_affiliation_string":"Queen's University Belfast, Belfast, United Kingdom","institution_ids":["https://openalex.org/I126231945"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101370351","display_name":"Bohan Lyu","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bohan Lyu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0003-2479-6314","affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100599817","display_name":"Hao Wang","orcid":"https://orcid.org/0000-0003-0896-080X"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hao Wang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, New Jersey, USA"],"raw_orcid":"https://orcid.org/0000-0002-1444-2657","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, New Jersey, USA","institution_ids":["https://openalex.org/I108468826"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100459489","display_name":"Tao Song","orcid":"https://orcid.org/0000-0002-5965-3140"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Song","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-5965-3140","affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049487451","display_name":"Haibing Guan","orcid":"https://orcid.org/0000-0002-4714-7400"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haibing Guan","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-4714-7400","affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"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":0.00021976,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1590","last_page":"1601"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10834","display_name":"Welding Techniques and Residual Stresses","score":0.05050000175833702,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10834","display_name":"Welding Techniques and Residual Stresses","score":0.05050000175833702,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12617","display_name":"Energy, Environment, and Transportation Policies","score":0.04410000145435333,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10597","display_name":"Nuclear reactor physics and engineering","score":0.0414000004529953,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.587165892124176},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5738752484321594},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5688857436180115},{"id":"https://openalex.org/keywords/language-change","display_name":"Language change","score":0.5258998274803162},{"id":"https://openalex.org/keywords/stage","display_name":"Stage (stratigraphy)","score":0.5188971161842346},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4988250732421875},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4940958321094513},{"id":"https://openalex.org/keywords/single-shot","display_name":"Single shot","score":0.490697979927063},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.48094576597213745},{"id":"https://openalex.org/keywords/fine-tuning","display_name":"Fine-tuning","score":0.471749871969223},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33100104331970215},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.33042120933532715},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.32049307227134705},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.23210030794143677},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.22232380509376526},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.1266050636768341},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.09690111875534058},{"id":"https://openalex.org/keywords/art","display_name":"Art","score":0.08040177822113037}],"concepts":[{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.587165892124176},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5738752484321594},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5688857436180115},{"id":"https://openalex.org/C2780027415","wikidata":"https://www.wikidata.org/wiki/Q524648","display_name":"Language change","level":2,"score":0.5258998274803162},{"id":"https://openalex.org/C146357865","wikidata":"https://www.wikidata.org/wiki/Q1123245","display_name":"Stage (stratigraphy)","level":2,"score":0.5188971161842346},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4988250732421875},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4940958321094513},{"id":"https://openalex.org/C3019835501","wikidata":"https://www.wikidata.org/wiki/Q1310130","display_name":"Single shot","level":2,"score":0.490697979927063},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.48094576597213745},{"id":"https://openalex.org/C157524613","wikidata":"https://www.wikidata.org/wiki/Q2828883","display_name":"Fine-tuning","level":2,"score":0.471749871969223},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33100104331970215},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.33042120933532715},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.32049307227134705},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.23210030794143677},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.22232380509376526},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1266050636768341},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.09690111875534058},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.08040177822113037},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1145/3770854.3780252","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3770854.3780252","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2405.19931","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.19931","pdf_url":"https://arxiv.org/pdf/2405.19931","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"pmh:doi:10.48550/arxiv.2405.19931","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.2405.19931","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2405.19931","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":"doi:10.1145/3770854.3780252","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3770854.3780252","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3142396426","https://openalex.org/W2471333042","https://openalex.org/W4396643691","https://openalex.org/W2955491601","https://openalex.org/W146529714","https://openalex.org/W2316500695","https://openalex.org/W2017914143","https://openalex.org/W146242624","https://openalex.org/W4396781972","https://openalex.org/W294285819"],"abstract_inverted_index":{"Few-shot":[0],"fine-tuning":[1,79,169],"of":[2,25,46,98,110,139,149,197],"Diffusion":[3],"Models":[4],"(DMs)":[5],"is":[6,163],"a":[7,102,154],"key":[8],"advancement,":[9],"significantly":[10,187],"reducing":[11],"training":[12,23,34],"costs":[13],"and":[14,27,81,133,153,173,190,195,204],"enabling":[15],"personalized":[16],"AI":[17],"applications.":[18],"However,":[19],"we":[20,72,93,116],"explore":[21],"the":[22,33,44,58,77,95,108,130,136,140,150,158,192,198],"dynamics":[24],"DMs":[26,123,172],"observe":[28],"an":[29,147],"unanticipated":[30],"phenomenon:":[31],"during":[32],"process,":[35],"image":[36],"fidelity":[37],"initially":[38],"improves,":[39],"then":[40,82],"unexpectedly":[41],"deteriorates":[42],"with":[43,53,60,124,157,166],"emergence":[45],"noisy":[47,62],"patterns,":[48],"only":[49],"to":[50,86,127],"recover":[51],"later":[52],"severe":[54],"overfitting.":[55],"We":[56],"term":[57],"stage":[59],"generated":[61,199],"patterns":[63],"as":[64,146],"corruption":[65,70,100],"stage.":[66],"To":[67,113],"understand":[68],"this":[69,84,91,99],"stage,":[71],"begin":[73],"by":[74],"heuristically":[75],"modeling":[76,85],"one-shot":[78],"scenario,":[80],"extend":[83],"more":[87],"general":[88],"cases.":[89],"Through":[90],"modeling,":[92],"identify":[94],"primary":[96],"cause":[97],"stage:":[101],"narrowed":[103],"learning":[104,137],"distribution":[105],"inherent":[106],"in":[107,171,201],"nature":[109],"few-shot":[111,168],"fine-tuning.":[112],"tackle":[114],"this,":[115],"apply":[117],"Bayesian":[118],"Neural":[119],"Networks":[120],"(BNNs)":[121],"on":[122],"variational":[125],"inference":[126,179],"implicitly":[128],"broaden":[129],"learned":[131],"distribution,":[132],"present":[134],"that":[135,184],"target":[138],"BNNs":[141],"can":[142],"be":[143],"naturally":[144],"regarded":[145],"expectation":[148],"diffusion":[151],"loss":[152],"further":[155],"regularization":[156],"pretrained":[159],"DMs.":[160],"This":[161],"approach":[162],"highly":[164],"compatible":[165],"current":[167],"methods":[170],"does":[174],"not":[175],"introduce":[176],"any":[177],"extra":[178],"costs.":[180],"Experimental":[181],"results":[182],"demonstrate":[183],"our":[185],"method":[186],"mitigates":[188],"corruption,":[189],"improves":[191],"fidelity,":[193],"quality":[194],"diversity":[196],"images":[200],"both":[202],"object-driven":[203],"subject-driven":[205],"generation":[206],"tasks.":[207]},"counts_by_year":[],"updated_date":"2026-08-02T14:50:37.381335","created_date":"2024-06-01T00:00:00"}
