{"id":"https://openalex.org/W7140180731","doi":"https://doi.org/10.48550/arxiv.2603.20584","title":"Improving Diffusion Generalization with Weak-to-Strong Segmented Guidance","display_name":"Improving Diffusion Generalization with Weak-to-Strong Segmented Guidance","publication_year":2026,"publication_date":"2026-03-21","ids":{"openalex":"https://openalex.org/W7140180731","doi":"https://doi.org/10.48550/arxiv.2603.20584"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.20584","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20584","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":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.20584","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yuan, Liangyu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yuan, Liangyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Huang, Yufei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Yufei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Lei, Mingkun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lei, Mingkun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zhao, Tong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Tong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Wang, Ruoyu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Ruoyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Chi, Changxi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chi, Changxi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Wang, Yiwei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yiwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Zhang, Chi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Chi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"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":0.40529999136924744,"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":0.40529999136924744,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.09130000323057175,"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"}},{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.07930000126361847,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/inference","display_name":"Inference","score":0.5645999908447266},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5486000180244446},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5357999801635742},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.5242999792098999},{"id":"https://openalex.org/keywords/iterative-and-incremental-development","display_name":"Iterative and incremental development","score":0.4268999993801117},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.3919000029563904},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.3370000123977661}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6861000061035156},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5645999908447266},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5486000180244446},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5357999801635742},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.5242999792098999},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48190000653266907},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44909998774528503},{"id":"https://openalex.org/C143587482","wikidata":"https://www.wikidata.org/wiki/Q1543216","display_name":"Iterative and incremental development","level":2,"score":0.4268999993801117},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3919000029563904},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.383899986743927},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.3370000123977661},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.313400000333786},{"id":"https://openalex.org/C201004817","wikidata":"https://www.wikidata.org/wiki/Q1707071","display_name":"Guidance system","level":2,"score":0.296999990940094},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2556999921798706}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.20584","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20584","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":"doi:10.48550/arxiv.2603.20584","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20584","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":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.78678959608078,"display_name":"Reduced inequalities"}],"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],"models":[1],"generate":[2],"synthetic":[3,95],"images":[4],"through":[5],"an":[6],"iterative":[7,19],"refinement":[8],"process.":[9],"However,":[10],"the":[11,14,18,27,55,66,82,101,113,129,132,140,150,154,190],"misalignment":[12],"between":[13,54],"simulation-free":[15],"objective":[16],"and":[17,35,47,57,99,110,174,193,199],"process":[20],"often":[21],"causes":[22],"accumulated":[23],"gradient":[24],"error":[25],"along":[26,143],"sampling":[28],"trajectory,":[29],"which":[30],"leads":[31],"to":[32,38,78,97],"unsatisfactory":[33],"results":[34],"a":[36,87,123],"failure":[37],"generalize.":[39],"Guidance":[40,45],"techniques":[41],"like":[42],"Classifier":[43],"Free":[44],"(CFG)":[46],"AutoGuidance":[48],"(AG)":[49],"alleviate":[50],"this":[51,90],"by":[52,108],"extrapolating":[53],"main":[56],"inferior":[58],"signal":[59],"for":[60],"stronger":[61],"generalization.":[62],"Despite":[63],"empirical":[64],"success,":[65],"effective":[67,102,191],"operational":[68],"regimes":[69],"of":[70,104,115,134,157],"prevalent":[71],"guidance":[72,84,105,182],"methods":[73,106],"are":[74],"still":[75],"under-explored,":[76],"leading":[77],"ambiguity":[79],"when":[80],"selecting":[81],"appropriate":[83],"method":[85],"given":[86],"precondition.":[88],"In":[89],"work,":[91],"we":[92,121,137],"first":[93],"conduct":[94],"comparisons":[96],"isolate":[98],"demonstrate":[100,138,189],"regime":[103],"represented":[107],"CFG":[109],"AG":[111],"from":[112],"perspective":[114],"weak-to-strong":[116],"principle.":[117],"Based":[118],"on":[119,172,186],"this,":[120],"propose":[122],"hybrid":[124],"instantiation":[125],"called":[126],"SGG":[127,145,178],"under":[128],"principle,":[130],"taking":[131],"benefits":[133],"both.":[135],"Furthermore,":[136],"that":[139,177],"W2S":[141],"principle":[142],"with":[144,165],"can":[146],"be":[147],"migrated":[148],"into":[149],"training":[151],"objective,":[152],"improving":[153],"generalization":[155],"ability":[156],"unguided":[158],"diffusion":[159],"models.":[160],"We":[161],"validate":[162],"our":[163],"approach":[164],"comprehensive":[166],"experiments.":[167],"At":[168],"inference":[169],"time,":[170],"evaluations":[171],"SD3":[173],"SD3.5":[175],"confirm":[176],"outperforms":[179],"existing":[180],"training-free":[181],"variants.":[183],"Training-time":[184],"experiments":[185],"transformer":[187],"architectures":[188],"migration":[192],"performance":[194],"gains":[195],"in":[196],"both":[197],"conditional":[198],"unconditional":[200],"settings.":[201],"Code":[202],"is":[203],"available":[204],"at":[205],"https://github.com/851695e35/SGG.":[206]},"counts_by_year":[],"updated_date":"2026-04-25T08:17:42.794288","created_date":"2026-03-25T00:00:00"}