{"id":"https://openalex.org/W4308236059","doi":"https://doi.org/10.1109/icip46576.2022.9897504","title":"Improving Robustness to out-of-Distribution Data by Frequency-Based Augmentation","display_name":"Improving Robustness to out-of-Distribution Data by Frequency-Based Augmentation","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4308236059","doi":"https://doi.org/10.1109/icip46576.2022.9897504"},"language":"en","primary_location":{"id":"doi:10.1109/icip46576.2022.9897504","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897504","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","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/A5112841339","display_name":"Koki Mukai","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Koki Mukai","raw_affiliation_strings":["The University of Tokyo"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090901020","display_name":"Soichiro Kumano","orcid":"https://orcid.org/0000-0002-3461-3943"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Soichiro Kumano","raw_affiliation_strings":["The University of Tokyo"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048624196","display_name":"Toshihiko Yamasaki","orcid":"https://orcid.org/0000-0002-1784-2314"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Toshihiko Yamasaki","raw_affiliation_strings":["The University of Tokyo"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5112841339"],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":0.9355,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.76815342,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3116","last_page":"3120"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9997000098228455,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9997000098228455,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9954000115394592,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.984000027179718,"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/robustness","display_name":"Robustness (evolution)","score":0.8701825141906738},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7172279357910156},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6382766366004944},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6291462182998657},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5287871956825256},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.49769237637519836},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.42745786905288696}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.8701825141906738},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7172279357910156},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6382766366004944},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6291462182998657},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5287871956825256},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.49769237637519836},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.42745786905288696},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip46576.2022.9897504","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897504","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","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":22,"referenced_works":["https://openalex.org/W569478347","https://openalex.org/W967544008","https://openalex.org/W1932198206","https://openalex.org/W2001610032","https://openalex.org/W2108598243","https://openalex.org/W2117539524","https://openalex.org/W2194775991","https://openalex.org/W2992308087","https://openalex.org/W3013557831","https://openalex.org/W3034175346","https://openalex.org/W3034230713","https://openalex.org/W3193940683","https://openalex.org/W4293846201","https://openalex.org/W4307823382","https://openalex.org/W6625168331","https://openalex.org/W6637162671","https://openalex.org/W6640425456","https://openalex.org/W6728622933","https://openalex.org/W6752760542","https://openalex.org/W6757555829","https://openalex.org/W6763810570","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W2770593030","https://openalex.org/W4321487865","https://openalex.org/W3154990682","https://openalex.org/W4313906399","https://openalex.org/W2560201613","https://openalex.org/W2171975302","https://openalex.org/W2022352247","https://openalex.org/W4285322112","https://openalex.org/W4292794239"],"abstract_inverted_index":{"Although":[0],"Convolutional":[1],"Neural":[2],"Networks":[3],"(CNNs)":[4],"have":[5],"high":[6],"accuracy":[7],"in":[8],"image":[9],"recognition,":[10],"they":[11],"are":[12,62,68],"vulnerable":[13],"to":[14,32,89,94],"adversarial":[15],"examples":[16],"and":[17,20,64,91],"out-of-distribution":[18,37,66,112],"data,":[19,38],"the":[21,34,48,55,59,65,70,79,83,108,121],"difference":[22],"from":[23,87],"human":[24],"recognition":[25],"has":[26],"been":[27],"pointed":[28],"out.":[29],"In":[30],"order":[31],"improve":[33],"robustness":[35],"against":[36],"we":[39,104],"present":[40],"a":[41,115],"frequency-based":[42],"data":[43,61,67,100,113],"augmentation":[44,101],"technique":[45],"that":[46,107],"replaces":[47],"frequency":[49],"components":[50,119],"with":[51,82,98],"other":[52],"images":[53],"of":[54,78,117,120],"same":[56],"class.":[57],"When":[58],"training":[60],"CIFAR10":[63],"SVHN,":[69],"Area":[71],"Under":[72],"Receiver":[73],"Operating":[74],"Characteristic":[75],"(AUROC)":[76],"curve":[77],"model":[80,110],"trained":[81],"proposed":[84],"method":[85],"increases":[86],"89.22%":[88],"98.15%,":[90],"further":[92],"increased":[93],"98.59%":[95],"when":[96],"combined":[97],"another":[99],"method.":[102],"Furthermore,":[103],"experimentally":[105],"demonstrate":[106],"robust":[109],"for":[111],"uses":[114],"lot":[116],"high-frequency":[118],"image.":[122]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
