{"id":"https://openalex.org/W7166518250","doi":"https://doi.org/10.48550/arxiv.2606.27696","title":"Class-frequency Guided Noise Schedule for Diffusion Models","display_name":"Class-frequency Guided Noise Schedule for Diffusion Models","publication_year":2026,"publication_date":"2026-06-26","ids":{"openalex":"https://openalex.org/W7166518250","doi":"https://doi.org/10.48550/arxiv.2606.27696"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.27696","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.27696","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.2606.27696","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016170160","display_name":"Jiequan Cui","orcid":"https://orcid.org/0000-0003-2422-7851"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cui, Jiequan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009738035","display_name":"Beier Zhu","orcid":"https://orcid.org/0000-0002-7900-6979"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Beier","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005356006","display_name":"Q. Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Qingshan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139630378","display_name":"Xiaojuan Qi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qi, Xiaojuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139557771","display_name":"Bei Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Bei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139614361","display_name":"Hanwang Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Hanwang","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":0.821399986743927,"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":0.821399986743927,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.02250000089406967,"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"}},{"id":"https://openalex.org/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.010999999940395355,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.7437999844551086},{"id":"https://openalex.org/keywords/schedule","display_name":"Schedule","score":0.7225000262260437},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5313000082969666},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.46230000257492065},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.42829999327659607},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4212000072002411},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.41679999232292175},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.3734000027179718}],"concepts":[{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.7437999844551086},{"id":"https://openalex.org/C68387754","wikidata":"https://www.wikidata.org/wiki/Q7271585","display_name":"Schedule","level":2,"score":0.7225000262260437},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6510000228881836},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5313000082969666},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.46230000257492065},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.42829999327659607},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4212000072002411},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.41679999232292175},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.40540000796318054},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3978999853134155},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.3734000027179718},{"id":"https://openalex.org/C200378446","wikidata":"https://www.wikidata.org/wiki/Q4147391","display_name":"Gradient noise","level":5,"score":0.357699990272522},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35429999232292175},{"id":"https://openalex.org/C35772409","wikidata":"https://www.wikidata.org/wiki/Q1323086","display_name":"Image noise","level":3,"score":0.3409999907016754},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.3206999897956848},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3199000060558319},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.31790000200271606},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.3012000024318695},{"id":"https://openalex.org/C182163834","wikidata":"https://www.wikidata.org/wiki/Q2926529","display_name":"Value noise","level":5,"score":0.29919999837875366},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28790000081062317},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2863999903202057},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2736999988555908},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.26899999380111694},{"id":"https://openalex.org/C114996537","wikidata":"https://www.wikidata.org/wiki/Q4854529","display_name":"Colors of noise","level":3,"score":0.26159998774528503},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.27696","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.27696","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.2606.27696","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.27696","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1,46,107],"paper,":[2],"we":[3,109,136,163],"are":[4],"the":[5,9,15,36,40,49,56,77,111,118,131,159,170],"first":[6],"to":[7,30,75],"examine":[8],"correlations":[10],"between":[11],"class":[12],"frequency":[13,174],"and":[14,102,147,155],"multi-scale":[16,41],"noise":[17,42,115,161,177],"schedule":[18,43,178],"within":[19,96],"diffusion":[20,50],"models.":[21],"For":[22],"score-based":[23],"generative":[24],"models,":[25],"low-density":[26,60],"regions":[27],"often":[28],"lead":[29],"inaccurately":[31],"estimated":[32,66],"scores,":[33],"thereby":[34],"compromising":[35],"generation":[37],"quality.":[38],"Although":[39],"can":[44],"alleviate":[45],"issue":[47],"during":[48],"process,":[51],"low-frequency":[52,97,121],"classes":[53,73,98,122],"still":[54],"face":[55],"challenge":[57],"of":[58,83,133,173],"large":[59],"regions,":[61],"resulting":[62],"in":[63,176],"more":[64],"inaccurate":[65],"scores":[67],"than":[68],"high-frequency":[69,72],"classes.":[70,92],"Furthermore,":[71],"tend":[74],"dominate":[76],"score":[78],"space,":[79],"causing":[80],"a":[81],"convergence":[82],"most":[84],"data":[85],"points":[86],"towards":[87],"generating":[88],"samples":[89,94],"from":[90],"these":[91],"Consequently,":[93],"generated":[95],"exhibit":[99],"suboptimal":[100],"quality":[101],"limited":[103],"diversity.":[104],"To":[105,129],"address":[106],"challenge,":[108],"propose":[110],"\\textit{Class-frequency":[112],"Guided":[113],"(CFRG)}":[114],"schedule,":[116,162],"leveraging":[117],"insight":[119],"that":[120],"should":[123],"be":[124],"endowed":[125],"with":[126],"larger-scale":[127],"noises.":[128],"illustrate":[130],"effectiveness":[132],"our":[134],"method,":[135],"conduct":[137],"experiments":[138],"on":[139],"various":[140],"tasks,":[141],"including":[142],"image":[143,145],"generation,":[144,149],"classification,":[146],"text-to-image":[148],"using":[150],"imbalanced":[151],"datasets,":[152],"\\textit{i.e.},":[153],"CIFAR-100-LT,":[154],"ImageNet-LT.":[156],"By":[157],"employing":[158],"CFRG":[160],"achieve":[164],"substantial":[165],"improvements":[166],"over":[167],"baselines,":[168],"manifesting":[169],"crucial":[171],"role":[172],"statistics":[175],"design.":[179]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-30T00:00:00"}