{"id":"https://openalex.org/W7127134199","doi":"https://doi.org/10.48550/arxiv.2601.22443","title":"Weak Diffusion Priors Can Still Achieve Strong Inverse-Problem Performance","display_name":"Weak Diffusion Priors Can Still Achieve Strong Inverse-Problem Performance","publication_year":2026,"publication_date":"2026-01-30","ids":{"openalex":"https://openalex.org/W7127134199","doi":"https://doi.org/10.48550/arxiv.2601.22443"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2601.22443","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":"preprint","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/A5124768333","display_name":"Jing Jia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jia, Jing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124788288","display_name":"Wei Yuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124827184","display_name":"Sifan Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Sifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124816937","display_name":"Liyue Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Liyue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5001630410","display_name":"Guanyang Wang","orcid":"https://orcid.org/0000-0001-7471-2869"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Guanyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"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/T11448","display_name":"Face recognition and analysis","score":0.6790000200271606,"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/T11448","display_name":"Face recognition and analysis","score":0.6790000200271606,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.16089999675750732,"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":0.052299998700618744,"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/prior-probability","display_name":"Prior probability","score":0.9592000246047974},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6495000123977661},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.6241000294685364},{"id":"https://openalex.org/keywords/inverse-problem","display_name":"Inverse problem","score":0.545799970626831},{"id":"https://openalex.org/keywords/inverse","display_name":"Inverse","score":0.49380001425743103},{"id":"https://openalex.org/keywords/prior-information","display_name":"Prior information","score":0.3424000144004822}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.9592000246047974},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6495000123977661},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.6241000294685364},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.545799970626831},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.49380001425743103},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4691999852657318},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.42890000343322754},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37689998745918274},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3750999867916107},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.3720000088214874},{"id":"https://openalex.org/C3020402766","wikidata":"https://www.wikidata.org/wiki/Q104376712","display_name":"Prior information","level":2,"score":0.3424000144004822},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3359000086784363},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.32820001244544983},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31189998984336853},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.311599999666214},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2946999967098236},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2601.22443","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.2601.22443","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.22443","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2601.22443","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Can":[0],"a":[1,25,43,147],"diffusion":[2,47,73,153],"model":[3,27],"trained":[4,28],"on":[5,29,150],"bedrooms":[6],"recover":[7],"human":[8],"faces?":[9],"Diffusion":[10],"models":[11],"are":[12,69,86],"widely":[13],"used":[14,157],"as":[15,56,58],"priors":[16,52,82,137,154],"for":[17],"inverse":[18,67],"problems,":[19],"but":[20],"standard":[21],"approaches":[22],"usually":[23],"assume":[24],"high-fidelity":[26],"data":[30],"that":[31,80,132],"closely":[32],"match":[33],"the":[34,111,120,124,128],"unknown":[35],"signal.":[36],"In":[37],"practice,":[38],"one":[39],"often":[40,53],"must":[41],"use":[42],"mismatched":[44],"or":[45],"low-fidelity":[46],"prior.":[48],"Surprisingly,":[49],"these":[50],"weak":[51,72,81,133,152],"perform":[54],"nearly":[55],"well":[57],"full-strength,":[59],"in-domain":[60],"baselines.":[61],"We":[62],"study":[63],"when":[64,84,151],"and":[65,93,134],"why":[66],"solvers":[68],"robust":[70],"to":[71],"priors.":[74],"Through":[75],"extensive":[76],"experiments,":[77],"we":[78,94,104],"find":[79],"succeed":[83],"measurements":[85,118],"highly":[87],"informative":[88],"(e.g.,":[89],"many":[90],"observed":[91],"pixels),":[92],"identify":[95],"regimes":[96],"where":[97],"they":[98],"fail.":[99],"To":[100],"explain":[101],"this":[102],"behavior,":[103],"combine":[105],"Bayesian-consistency":[106],"theory":[107,112],"with":[108],"local-correlation":[109],"analysis:":[110],"gives":[113],"conditions":[114],"under":[115],"which":[116],"high-dimensional":[117],"make":[119],"posterior":[121],"concentrate":[122],"near":[123],"true":[125],"signal,":[126],"while":[127],"correlation":[129],"analysis":[130],"shows":[131],"stronger":[135],"natural-image":[136],"can":[138,155],"share":[139],"similar":[140],"local":[141],"spatial":[142],"structure.":[143],"These":[144],"results":[145],"provide":[146],"principled":[148],"justification":[149],"be":[156],"reliably.":[158],"Code":[159],"is":[160],"available":[161],"at":[162],"https://github.com/jjia131/weak-diffusion-priors-inverse-problem.":[163]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-02-03T00:00:00"}
