{"id":"https://openalex.org/W3206300324","doi":null,"title":"Unveiling the Power of Mixup for Stronger Classifiers.","display_name":"Unveiling the Power of Mixup for Stronger Classifiers.","publication_year":2021,"publication_date":"2021-10-12","ids":{"openalex":"https://openalex.org/W3206300324","mag":"3206300324"},"language":"en","primary_location":{"id":"mag:3206300324","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2103.13027.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2103.13027.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101728120","display_name":"Zicheng Liu","orcid":"https://orcid.org/0000-0003-1106-2963","observed_orcids":["https://orcid.org/0000-0003-1106-2963"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zicheng Liu","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-1106-2963","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100406638","display_name":"Siyuan Li","orcid":"https://orcid.org/0009-0006-8905-0064","observed_orcids":["https://orcid.org/0009-0006-8905-0064","https://orcid.org/0000-0002-9999-437X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Siyuan Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100599890","display_name":"Di Wu","orcid":"https://orcid.org/0000-0002-4753-8161","observed_orcids":["https://orcid.org/0000-0002-4753-8161","https://orcid.org/0000-0001-6589-7136"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Di Wu","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-4753-8161","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100438521","display_name":"Zhiyuan Chen","orcid":"https://orcid.org/0000-0003-3210-0324","observed_orcids":["https://orcid.org/0000-0003-3210-0324","https://orcid.org/0009-0004-1893-7499","https://orcid.org/0000-0001-6890-854X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhiyuan Chen","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-3210-0324","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041519364","display_name":"Lirong Wu","orcid":"https://orcid.org/0000-0001-5551-3194","observed_orcids":["https://orcid.org/0000-0001-5551-3194"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lirong Wu","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-5551-3194","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068175470","display_name":"Jianzhu Guo","orcid":"https://orcid.org/0000-0002-8493-3689","observed_orcids":["https://orcid.org/0000-0002-8493-3689"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jianzhu Guo","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-8493-3689","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5082786719","display_name":"Stan Ziqing Li","orcid":"https://orcid.org/0000-0002-2961-8096","observed_orcids":["https://orcid.org/0000-0002-2961-8096"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stan Z. Li","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-2961-8096","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":3,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.5386000275611877,"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":0.5386000275611877,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.319599986076355,"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":0.12359999865293503,"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/mixup","display_name":"mixup","score":0.8159999847412109},{"id":"https://openalex.org/keywords/data-augmentation","display_name":"data augmentation","score":0.8880000114440918},{"id":"https://openalex.org/keywords/cross-attention","display_name":"cross-attention","score":0.9430000185966492},{"id":"https://openalex.org/keywords/training-stability","display_name":"training stability","score":0.9039999842643738},{"id":"https://openalex.org/keywords/supervised-classification","display_name":"supervised classification","score":0.5109999775886536}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7629817724227905},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.7090710401535034},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6887996792793274},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.6693305969238281},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6185126900672913},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.5983456373214722},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5857429504394531},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5723458528518677},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5092161893844604},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.45020949840545654},{"id":"https://openalex.org/C2776482837","wikidata":"https://www.wikidata.org/wiki/Q3553958","display_name":"Multi-label classification","level":2,"score":0.44539690017700195},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4406718611717224},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.43058010935783386},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.42535650730133057},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3585653603076935},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.23677599430084229},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10728946328163147},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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":"mag:3206300324","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2103.13027.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:3206300324","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2103.13027.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1663973292","https://openalex.org/W1797268635","https://openalex.org/W1846799578","https://openalex.org/W1901129140","https://openalex.org/W2095705004","https://openalex.org/W2104094955","https://openalex.org/W2117539524","https://openalex.org/W2194775991","https://openalex.org/W2257979135","https://openalex.org/W2412782625","https://openalex.org/W2518108298","https://openalex.org/W2549139847","https://openalex.org/W2626967530","https://openalex.org/W2741430497","https://openalex.org/W2765407302","https://openalex.org/W2945125794","https://openalex.org/W2962858109","https://openalex.org/W2963060032","https://openalex.org/W2963341956","https://openalex.org/W2963351448","https://openalex.org/W2992308087","https://openalex.org/W2994931756","https://openalex.org/W3035224233","https://openalex.org/W3035524453","https://openalex.org/W3035712793","https://openalex.org/W3037204200","https://openalex.org/W3049737268","https://openalex.org/W3114400232","https://openalex.org/W3118608800","https://openalex.org/W3127819337","https://openalex.org/W3128324120","https://openalex.org/W4283794091"],"related_works":["https://openalex.org/W4312869703","https://openalex.org/W3139278653","https://openalex.org/W4390042759","https://openalex.org/W2891021639","https://openalex.org/W4377121352","https://openalex.org/W4221149927","https://openalex.org/W3216446504","https://openalex.org/W4388972906","https://openalex.org/W2997205428","https://openalex.org/W4403617799"],"abstract_inverted_index":{"Mixup-based":[0],"data":[1],"augmentations":[2],"have":[3],"achieved":[4],"great":[5],"success":[6],"as":[7,99],"regularizers":[8],"for":[9,96],"deep":[10],"neural":[11],"networks.":[12],"However,":[13],"existing":[14],"methods":[15,40,189],"rely":[16],"on":[17,53,164,176],"deliberately":[18],"handcrafted":[19],"mixup":[20,79,95,102,114],"policies,":[21],"which":[22],"ignore":[23],"or":[24],"oversell":[25],"the":[26,64,75,85,140,148,153,168,183],"semantic":[27],"matching":[28],"between":[29,139],"mixed":[30,132,150],"samples":[31,143],"and":[32,105,161],"labels.":[33],"Driven":[34],"by":[35,46,84,134,190],"their":[36],"prior":[37],"assumptions,":[38],"early":[39],"attempt":[41],"to":[42,129],"smooth":[43],"decision":[44],"boundaries":[45],"random":[47],"linear":[48],"interpolation":[49],"while":[50],"others":[51],"focus":[52],"maximizing":[54],"class-related":[55],"information":[56],"via":[57],"offline":[58],"saliency":[59],"optimization.":[60],"As":[61],"a":[62,112,117,124,131,136,191],"result,":[63],"issue":[65],"of":[66,78,142,147,166],"label":[67,86],"mismatch":[68],"has":[69],"not":[70],"been":[71],"well":[72],"addressed.":[73],"Additionally,":[74],"optimization":[76],"stability":[77,160],"training":[80,159],"is":[81,127],"constantly":[82],"troubled":[83],"mismatch.":[87],"To":[88],"address":[89],"these":[90],"challenges,":[91],"we":[92],"first":[93],"reformulate":[94],"supervised":[97],"classification":[98,179],"two":[100],"sub-tasks,":[101],"sample":[103,133],"generation":[104],"classification,":[106],"then":[107],"propose":[108],"Automatic":[109],"Mixup":[110],"(AutoMix),":[111],"revolutionary":[113],"framework.":[115],"Specifically,":[116],"learnable":[118],"lightweight":[119],"Mix":[120,169],"Block":[121,170],"(MB)":[122],"with":[123],"cross-attention":[125],"mechanism":[126],"proposed":[128,154,184],"generate":[130],"modeling":[135],"fair":[137],"relationship":[138],"pair":[141],"under":[144],"direct":[145],"supervision":[146],"corresponding":[149],"label.":[151],"Moreover,":[152],"Momentum":[155],"Pipeline":[156],"(MP)":[157],"enhances":[158],"accelerates":[162],"convergence":[163],"top":[165],"making":[167],"fully":[171],"trained":[172],"end-to-end.":[173],"Extensive":[174],"experiments":[175],"five":[177],"popular":[178],"benchmarks":[180],"show":[181],"that":[182],"approach":[185],"consistently":[186],"outperforms":[187],"leading":[188],"large":[192],"margin.":[193]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-11-05T12:25:35.769452","created_date":"2021-10-25T00:00:00"}
