{"id":"https://openalex.org/W4416251124","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229392","title":"Dynamic RDP-based Sampling for Faster Diffusion Models","display_name":"Dynamic RDP-based Sampling for Faster Diffusion Models","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251124","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229392"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11229392","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229392","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000212634","display_name":"Yi Yin","orcid":"https://orcid.org/0000-0003-4750-4997"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yongcheng Yin","raw_affiliation_strings":["Renmin University of China,School of Agricultural Economics and Rural Development,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Renmin University of China,School of Agricultural Economics and Rural Development,Beijing,China","institution_ids":["https://openalex.org/I78988378"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5000212634"],"corresponding_institution_ids":["https://openalex.org/I78988378"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.31268713,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"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":0.5716000199317932,"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":0.5716000199317932,"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":0.1306000053882599,"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/T11448","display_name":"Face recognition and analysis","score":0.03799999877810478,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.7283999919891357},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7275000214576721},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6248000264167786},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5684000253677368},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.45660001039505005},{"id":"https://openalex.org/keywords/oversampling","display_name":"Oversampling","score":0.4507000148296356},{"id":"https://openalex.org/keywords/adaptive-sampling","display_name":"Adaptive sampling","score":0.4309999942779541}],"concepts":[{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.7283999919891357},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7275000214576721},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.694599986076355},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6248000264167786},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5684000253677368},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.45660001039505005},{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.4507000148296356},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.4309999942779541},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.41100001335144043},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4049000144004822},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.4043999910354614},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39640000462532043},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3873000144958496},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.3865000009536743},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3718999922275543},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35920000076293945},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35510000586509705},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.305400013923645},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.29980000853538513},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.26510000228881836},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.251800000667572}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11229392","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229392","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W1981934656","https://openalex.org/W2078807908","https://openalex.org/W2108598243","https://openalex.org/W2155195832","https://openalex.org/W4251696430","https://openalex.org/W4312933868","https://openalex.org/W4402704590","https://openalex.org/W4402727382","https://openalex.org/W4403757510"],"related_works":[],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"diffusion-based":[3],"models":[4,15],"have":[5],"achieved":[6],"state-of-the-art":[7],"performance":[8],"in":[9,62,129,191],"generative":[10,169],"tasks.":[11],"However,":[12],"existing":[13],"diffusion":[14,51,60],"face":[16],"the":[17,21,59,68,73,89,97,121,133,144,151,173,182,186,192],"following":[18],"issues:":[19],"(1)":[20],"multi-step":[22],"sampling":[23,100,122,177],"process":[24,61,123],"is":[25,37],"computationally":[26],"intensive;":[27],"(2)":[28],"to":[29,72,131,172],"ensure":[30],"coverage":[31],"across":[32],"diverse":[33],"domains,":[34],"generation":[35,145],"quality":[36,170],"often":[38],"sacrificed.":[39],"To":[40],"address":[41],"these":[42,107,125],"challenges,":[43],"this":[44],"paper":[45],"proposes":[46],"a":[47,63,136],"novel":[48],"domain-distillation-based":[49],"accelerated":[50],"model":[52],"method\u2014Dynamic":[53],"RDP-based":[54],"Sampling":[55],"(DRS).":[56],"Specifically,":[57],"during":[58],"targeted":[64],"domain,":[65,138],"we":[66,81,110,139],"apply":[67],"Ramer\u2013Douglas\u2013Peucker":[69],"(RDP)":[70],"algorithm":[71],"intermediate":[74],"latent":[75],"representations.":[76],"By":[77,105],"analyzing":[78,106],"pixel":[79],"trajectories,":[80],"identify":[82,112],"and":[83],"remove":[84],"redundant":[85],"information,":[86],"retaining":[87],"only":[88],"most":[90],"important":[91],"data":[92],"points,":[93],"thereby":[94],"effectively":[95],"reducing":[96],"number":[98],"of":[99,135,188],"steps":[101,115,153],"required":[102],"for":[103],"generation.":[104],"simplified":[108],"representations,":[109],"can":[111],"critical":[113],"time":[114,152],"where":[116],"significant":[117],"changes":[118],"occur,":[119],"focusing":[120],"on":[124,143,158,181],"key":[126],"moments.":[127],"Furthermore,":[128],"order":[130],"improve":[132],"generalization":[134,187],"specific":[137,193],"perform":[140],"distillation":[141],"based":[142],"results":[146,180],"guided":[147],"by":[148],"DRS,":[149],"emphasizing":[150],"with":[154],"higher":[155],"importance.":[156],"Results":[157],"benchmark":[159],"datasets":[160],"demonstrate":[161],"that":[162],"DRS":[163],"improves":[164],"computational":[165],"efficiency":[166],"while":[167],"maintaining":[168],"comparable":[171],"standard":[174],"T":[175],"-step":[176],"schedule.":[178],"Experimental":[179],"food":[183],"field":[184],"validate":[185],"our":[189],"method":[190],"domain.":[194]},"counts_by_year":[],"updated_date":"2025-11-28T22:10:10.138306","created_date":"2025-11-14T00:00:00"}