{"id":"https://openalex.org/W3034200289","doi":"https://doi.org/10.1109/cvpr42600.2020.00045","title":"Auxiliary Training: Towards Accurate and Robust Models","display_name":"Auxiliary Training: Towards Accurate and Robust Models","publication_year":2020,"publication_date":"2020-06-01","ids":{"openalex":"https://openalex.org/W3034200289","doi":"https://doi.org/10.1109/cvpr42600.2020.00045","mag":"3034200289"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr42600.2020.00045","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr42600.2020.00045","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","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/A5100689114","display_name":"Linfeng Zhang","orcid":"https://orcid.org/0000-0002-3341-183X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linfeng Zhang","raw_affiliation_strings":["Institute for interdisciplinary Information Core Technology","Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for interdisciplinary Information Core Technology","institution_ids":[]},{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070052121","display_name":"Muzhou Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Muzhou Yu","raw_affiliation_strings":["Institute for interdisciplinary Information Core Technology","Xi'an Jiaotong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for interdisciplinary Information Core Technology","institution_ids":[]},{"raw_affiliation_string":"Xi'an Jiaotong University","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015527515","display_name":"Tong Chen","orcid":"https://orcid.org/0000-0003-3805-4138"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tong Chen","raw_affiliation_strings":["Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077724126","display_name":"Zuoqiang Shi","orcid":"https://orcid.org/0000-0002-9122-0302"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zuoqiang Shi","raw_affiliation_strings":["Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054041500","display_name":"Chenglong Bao","orcid":"https://orcid.org/0000-0002-1201-1212"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenglong Bao","raw_affiliation_strings":["Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006570986","display_name":"Kaisheng Ma","orcid":"https://orcid.org/0000-0001-9226-3366"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaisheng Ma","raw_affiliation_strings":["Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.3915,"has_fulltext":false,"cited_by_count":36,"citation_normalized_percentile":{"value":0.92264574,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"369","last_page":"378"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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":0.9994000196456909,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9947999715805054,"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/computer-science","display_name":"Computer science","score":0.731397271156311},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6745922565460205},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5452072024345398},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5313186049461365},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44954627752304077},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4425256848335266},{"id":"https://openalex.org/keywords/margin-classifier","display_name":"Margin classifier","score":0.4178030788898468},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.376729279756546}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.731397271156311},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6745922565460205},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5452072024345398},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5313186049461365},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44954627752304077},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4425256848335266},{"id":"https://openalex.org/C173102733","wikidata":"https://www.wikidata.org/wiki/Q6760396","display_name":"Margin classifier","level":3,"score":0.4178030788898468},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.376729279756546},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvpr42600.2020.00045","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr42600.2020.00045","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8100000023841858,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":85,"referenced_works":["https://openalex.org/W1800306869","https://openalex.org/W1836465849","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1945616565","https://openalex.org/W2067562626","https://openalex.org/W2108598243","https://openalex.org/W2133564696","https://openalex.org/W2150287743","https://openalex.org/W2156163116","https://openalex.org/W2163605009","https://openalex.org/W2165644552","https://openalex.org/W2194775991","https://openalex.org/W2253986341","https://openalex.org/W2342045095","https://openalex.org/W2549139847","https://openalex.org/W2618530766","https://openalex.org/W2746314669","https://openalex.org/W2774644650","https://openalex.org/W2794356495","https://openalex.org/W2804047946","https://openalex.org/W2896457183","https://openalex.org/W2905631704","https://openalex.org/W2942801205","https://openalex.org/W2948636190","https://openalex.org/W2948805184","https://openalex.org/W2949117887","https://openalex.org/W2953327099","https://openalex.org/W2954540134","https://openalex.org/W2961540362","https://openalex.org/W2962946266","https://openalex.org/W2963060032","https://openalex.org/W2963136028","https://openalex.org/W2963150697","https://openalex.org/W2963207607","https://openalex.org/W2963341956","https://openalex.org/W2963389226","https://openalex.org/W2963403868","https://openalex.org/W2963430933","https://openalex.org/W2963446712","https://openalex.org/W2963495494","https://openalex.org/W2963693747","https://openalex.org/W2963877604","https://openalex.org/W2964137095","https://openalex.org/W2964253222","https://openalex.org/W2964308564","https://openalex.org/W2970406152","https://openalex.org/W2971028215","https://openalex.org/W2971688585","https://openalex.org/W2989929945","https://openalex.org/W2998243512","https://openalex.org/W3106250896","https://openalex.org/W3106297436","https://openalex.org/W3118608800","https://openalex.org/W4288281368","https://openalex.org/W4288335398","https://openalex.org/W4288347505","https://openalex.org/W4288359223","https://openalex.org/W4288404646","https://openalex.org/W4293390340","https://openalex.org/W4293846201","https://openalex.org/W4295727797","https://openalex.org/W4385245566","https://openalex.org/W6638667902","https://openalex.org/W6639824700","https://openalex.org/W6640425456","https://openalex.org/W6679434410","https://openalex.org/W6682365476","https://openalex.org/W6684671274","https://openalex.org/W6713132643","https://openalex.org/W6739868092","https://openalex.org/W6739901393","https://openalex.org/W6749966379","https://openalex.org/W6750189243","https://openalex.org/W6751795773","https://openalex.org/W6755207826","https://openalex.org/W6761830755","https://openalex.org/W6762947505","https://openalex.org/W6763136721","https://openalex.org/W6763260857","https://openalex.org/W6763810570","https://openalex.org/W6765696844","https://openalex.org/W6768086124","https://openalex.org/W6785652829","https://openalex.org/W6786286779"],"related_works":["https://openalex.org/W2297694731","https://openalex.org/W206493657","https://openalex.org/W1964081096","https://openalex.org/W1501134308","https://openalex.org/W1483596504","https://openalex.org/W2554106811","https://openalex.org/W204488290","https://openalex.org/W2009506202","https://openalex.org/W2010370304","https://openalex.org/W1984354327"],"abstract_inverted_index":{"Training":[0],"process":[1],"is":[2,107,133,155,181],"crucial":[3],"for":[4,55,79],"the":[5,8,40,46,49,60,76,85,92,96,111,116,137,140,147,158,173,178,212],"deployment":[6],"of":[7,42,48,143,149,160,175],"network":[9],"in":[10,27],"applications":[11],"which":[12],"have":[13],"two":[14],"strict":[15],"requirements":[16],"on":[17,81,194,203,235,241],"both":[18,204],"accuracy":[19,32,61,205,222],"and":[20,33,62,162,183,188,197,206,223],"robustness.":[21],"However,":[22],"most":[23],"existing":[24],"approaches":[25],"are":[26,88,169,190],"a":[28,70,99,125,152],"dilemma,":[29],"i.e.":[30],"model":[31,138],"robustness":[34,63,207,225],"form":[35],"an":[36],"embarrassing":[37],"tradeoff":[38],"-":[39,129],"improvement":[41],"one":[43],"leads":[44],"to":[45,58,109,114,135,157],"drop":[47],"other.":[50],"The":[51],"challenge":[52],"remains":[53],"as":[54],"we":[56,68],"try":[57],"improve":[59],"simultaneously.":[64],"In":[65,95,172],"this":[66],"paper,":[67],"propose":[69],"novel":[71,100],"training":[72,80,97,150,219,233],"method":[73,102,128],"via":[74],"introducing":[75],"auxiliary":[77,120,163,214,218],"classifiers":[78,164],"corrupted":[82,144],"samples,":[83],"while":[84],"clean":[86],"samples":[87],"normally":[89],"trained":[90],"with":[91,123],"primary":[93,112,161,179],"classifier.":[94],"stage,":[98],"distillation":[101,106],"named":[103],"input-aware":[104],"self":[105],"proposed":[108,134,213],"facilitate":[110],"classifier":[113,180],"learn":[115],"robust":[117],"information":[118],"from":[119,139],"classifiers.":[121],"Along":[122],"it,":[124],"new":[126],"normalization":[127,132],"selective":[130],"batch":[131],"prevent":[136],"negative":[141],"influence":[142],"images.":[145],"At":[146],"end":[148],"period,":[151],"L2-norm":[153],"penalty":[154],"applied":[156],"weights":[159,168],"such":[165],"that":[166,200],"their":[167],"asymptotically":[170],"identical.":[171],"stage":[174],"inference,":[176],"only":[177],"used":[182],"thus":[184],"no":[185],"extra":[186],"computation":[187],"storage":[189],"needed.":[191],"Extensive":[192],"experiments":[193],"CIFAR10,":[195],"CIFAR100":[196],"ImageNet":[198],"show":[199],"noticeable":[201],"improvements":[202,230],"can":[208],"be":[209],"observed":[210],"by":[211,227],"training.":[215],"On":[216],"average,":[217],"achieves":[220],"2.21%":[221],"21.64%":[224],"(measured":[226],"corruption":[228],"error)":[229],"over":[231],"traditional":[232],"methods":[234],"CIFAR100.":[236],"Codes":[237],"has":[238],"been":[239],"released":[240],"github.":[242]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":1}],"updated_date":"2026-08-06T08:24:18.245995","created_date":"2025-10-10T00:00:00"}
