{"id":"https://openalex.org/W7162767770","doi":"https://doi.org/10.48550/arxiv.2605.28962","title":"Resolving Endpoint Underfitting in Diffusion Bridges via Noise Alignment","display_name":"Resolving Endpoint Underfitting in Diffusion Bridges via Noise Alignment","publication_year":2026,"publication_date":"2026-05-27","ids":{"openalex":"https://openalex.org/W7162767770","doi":"https://doi.org/10.48550/arxiv.2605.28962"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.28962","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28962","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.28962","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137392717","display_name":"Yurong Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Yurong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137351798","display_name":"Zicheng Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zicheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046469888","display_name":"Congying Han","orcid":"https://orcid.org/0000-0002-3445-4620"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Congying","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137378010","display_name":"Tiande Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Tiande","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5035749919","display_name":"Xinmin Qiu","orcid":"https://orcid.org/0009-0007-8820-5797"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiu, Xinmin","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":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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9133999943733215,"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":0.9133999943733215,"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/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.013500000350177288,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.010999999940395355,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.7322999835014343},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.6660000085830688},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.6233999729156494},{"id":"https://openalex.org/keywords/diffusion-process","display_name":"Diffusion process","score":0.5199999809265137},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.5178999900817871},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5059000253677368},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4821000099182129},{"id":"https://openalex.org/keywords/translation","display_name":"Translation (biology)","score":0.4722999930381775},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.43709999322891235}],"concepts":[{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.7322999835014343},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.6660000085830688},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.6233999729156494},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5902000069618225},{"id":"https://openalex.org/C68710425","wikidata":"https://www.wikidata.org/wiki/Q5275442","display_name":"Diffusion process","level":3,"score":0.5199999809265137},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5178999900817871},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5059000253677368},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4821000099182129},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.4722999930381775},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4521999955177307},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4521999955177307},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.43709999322891235},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.3285999894142151},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.320499986410141},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.3149000108242035},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.311599999666214},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.3005000054836273},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.29330000281333923},{"id":"https://openalex.org/C35772409","wikidata":"https://www.wikidata.org/wiki/Q1323086","display_name":"Image noise","level":3,"score":0.2913999855518341},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2761000096797943},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2703000009059906},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.26510000228881836},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C2779757391","wikidata":"https://www.wikidata.org/wiki/Q6002292","display_name":"Image translation","level":3,"score":0.25690001249313354},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.2549999952316284}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.28962","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28962","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.28962","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28962","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.7175215482711792,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Diffusion":[0,98],"bridge":[1,24,105],"models":[2],"offer":[3],"a":[4,109,114,121],"powerful":[5],"framework":[6],"for":[7],"connecting":[8],"two":[9],"data":[10],"distributions,":[11],"such":[12],"as":[13,52],"in":[14,68,80],"image":[15,145,148],"restoration":[16,146],"and":[17,72,87,118,133,147],"translation.":[18],"Many":[19],"existing":[20],"methods":[21],"learn":[22],"this":[23,35,40,92],"by":[25,65,106],"mimicking":[26],"the":[27,49,53,56,69,84,96,103,130,135,138,152],"score-matching":[28],"formulation":[29,128],"of":[30,154],"standard":[31],"diffusion":[32,104],"models.":[33],"In":[34],"work,":[36],"we":[37,94],"find":[38],"that":[39],"way":[41],"leads":[42],"to":[43,112],"an":[44,76],"anomalous":[45],"underfitting":[46,136],"phenomenon":[47],"near":[48,137],"target":[50,57,139],"endpoint,":[51],"process":[54],"approaches":[55],"distribution":[58],"($t":[59],"\\to":[60],"0$).":[61],"This":[62,126],"underfitting,":[63],"characterized":[64],"significant":[66],"drift":[67],"predicted":[70],"variance":[71],"direction,":[73],"results":[74],"from":[75],"excessively":[77],"large":[78],"discrepancy":[79],"noise":[81,131],"levels":[82],"between":[83],"network's":[85],"input":[86],"its":[88],"regression":[89],"target.To":[90],"resolve":[91],"issue,":[93],"propose":[95],"Noise-Aligned":[97],"Bridge":[99],"(NADB).Our":[100],"approach":[101],"reformulates":[102],"first":[107],"employing":[108],"mean":[110],"network":[111],"provide":[113],"cleaner":[115],"conditional":[116],"target,":[117],"then":[119],"introducing":[120],"novel,":[122],"noise-aligned":[123],"mapping":[124],"relationship.":[125],"new":[127],"resolves":[129],"mismatch":[132],"corrects":[134],"endpoint.":[140],"Experimental":[141],"validation":[142],"across":[143],"multiple":[144],"translation":[149],"tasks":[150],"demonstrates":[151],"effectiveness":[153],"our":[155],"approach.":[156],"Code":[157],"is":[158],"available":[159],"at":[160],"https://github.com/gyr02/NADB.":[161]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-30T00:00:00"}
