{"id":"https://openalex.org/W4415709576","doi":"https://doi.org/10.1109/icme59968.2025.11209463","title":"Double-Shrink: Enhancing Model Robustness under SDN Noise by Reducing Uncertain Confidence","display_name":"Double-Shrink: Enhancing Model Robustness under SDN Noise by Reducing Uncertain Confidence","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4415709576","doi":"https://doi.org/10.1109/icme59968.2025.11209463"},"language":null,"primary_location":{"id":"doi:10.1109/icme59968.2025.11209463","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11209463","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5111728372","display_name":"N. Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Naihao Wang","raw_affiliation_strings":["Beijing University of Chemical Technology,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Chemical Technology,Beijing,China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008924751","display_name":"Can Yang Zhang","orcid":"https://orcid.org/0000-0002-6975-5781"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Can Zhang","raw_affiliation_strings":["Beijing University of Chemical Technology,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Chemical Technology,Beijing,China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100357478","display_name":"Yunfeng Liu","orcid":"https://orcid.org/0000-0001-8487-0078"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunfeng Liu","raw_affiliation_strings":["Beijing University of Chemical Technology,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Chemical Technology,Beijing,China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032979332","display_name":"Wentao Chen","orcid":"https://orcid.org/0009-0009-9913-3279"},"institutions":[{"id":"https://openalex.org/I181877577","display_name":"Shanxi University","ror":"https://ror.org/03y3e3s17","country_code":"CN","type":"education","lineage":["https://openalex.org/I181877577"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wentao Chen","raw_affiliation_strings":["Shanxi University,Shanxi,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanxi University,Shanxi,China","institution_ids":["https://openalex.org/I181877577"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101737631","display_name":"Ruirui Li","orcid":"https://orcid.org/0009-0002-4635-6128"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruirui Li","raw_affiliation_strings":["Beijing University of Chemical Technology,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Chemical Technology,Beijing,China","institution_ids":["https://openalex.org/I75390827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9605000019073486,"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"}},"topics":[{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9605000019073486,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.007199999876320362,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.004699999932199717,"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/robustness","display_name":"Robustness (evolution)","score":0.6790000200271606},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.63919997215271},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5074999928474426},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.41850000619888306},{"id":"https://openalex.org/keywords/unavailability","display_name":"Unavailability","score":0.374099999666214},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.37380000948905945},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.37049999833106995},{"id":"https://openalex.org/keywords/noise-immunity","display_name":"Noise immunity","score":0.36730000376701355}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7516999840736389},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6790000200271606},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.63919997215271},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5074999928474426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49900001287460327},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.453900009393692},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.41850000619888306},{"id":"https://openalex.org/C2780505938","wikidata":"https://www.wikidata.org/wiki/Q17093282","display_name":"Unavailability","level":2,"score":0.374099999666214},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.37380000948905945},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.37049999833106995},{"id":"https://openalex.org/C2988494973","wikidata":"https://www.wikidata.org/wiki/Q179448","display_name":"Noise immunity","level":3,"score":0.36730000376701355},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.33059999346733093},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.3246999979019165},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.3122999966144562},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.31200000643730164},{"id":"https://openalex.org/C30038468","wikidata":"https://www.wikidata.org/wiki/Q4354775","display_name":"Memorization","level":2,"score":0.30250000953674316},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2833000123500824},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.28139999508857727},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.2587999999523163},{"id":"https://openalex.org/C2780909371","wikidata":"https://www.wikidata.org/wiki/Q4801092","display_name":"Artificial noise","level":4,"score":0.25769999623298645}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme59968.2025.11209463","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11209463","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1921293667","https://openalex.org/W2162602080","https://openalex.org/W2194775991","https://openalex.org/W2302255633","https://openalex.org/W3043426275","https://openalex.org/W3103649165","https://openalex.org/W4287947470","https://openalex.org/W4313068152","https://openalex.org/W4382318341","https://openalex.org/W4386071642"],"related_works":[],"abstract_inverted_index":{"Label":[0],"noise":[1,37,52,109],"can":[2,53,102],"degrade":[3],"the":[4,12,59,66,82,87,114,127],"generalization":[5],"performance":[6,115],"of":[7,14,34,51,62,142],"deep":[8],"neural":[9],"networks,":[10],"making":[11],"study":[13],"methods":[15,138],"to":[16,77],"alleviate":[17],"its":[18,69,79],"negative":[19],"impact":[20],"an":[21,74],"important":[22],"research":[23],"focus.":[24],"In":[25],"this":[26],"paper,":[27],"we":[28],"focus":[29],"on":[30,126],"a":[31,98],"novel":[32],"type":[33,50],"noise\u2014subclass":[35],"dominant":[36],"(SDN)\u2014which":[38],"is":[39,95],"prevalent":[40],"in":[41],"real-world":[42],"datasets":[43],"but":[44],"has":[45],"previously":[46],"been":[47],"overlooked.":[48],"This":[49,91],"be":[54,147],"learned":[55],"and":[56,72,86,123,130],"memorized":[57],"during":[58],"early":[60,70],"stages":[61],"training.":[63],"We":[64,120],"analyze":[65],"reasons":[67],"behind":[68],"memorization":[71],"propose":[73],"effective":[75],"approach":[76],"mitigate":[78],"influence:":[80],"shrinking":[81],"confusing":[83],"class":[84],"space":[85],"uncertain":[88],"style":[89],"features.":[90],"dual-dimensional":[92],"shrinkage":[93],"method":[94],"designed":[96],"as":[97],"plug-and-play":[99],"module":[100],"that":[101],"seamlessly":[103],"integrate":[104],"with":[105],"existing":[106],"sample-selection-based":[107],"label":[108],"learning":[110],"methods,":[111],"effectively":[112],"addressing":[113],"degradation":[116],"caused":[117],"by":[118],"SDN.":[119],"conducted":[121],"comparative":[122],"ablation":[124],"experiments":[125],"synthetic":[128],"CIFAR20-SDN":[129],"Clothing1M":[131],"datasets.":[132],"Our":[133,144],"framework":[134],"consistently":[135],"outperformed":[136],"state-of-the-art":[137],"under":[139],"various":[140],"levels":[141],"noise.":[143],"code":[145],"will":[146],"open-sourced":[148],"soon.":[149]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-30T00:00:00"}