{"id":"https://openalex.org/W4312364074","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892528","title":"A General Multiple Data Augmentation Based Framework for Training Deep Neural Networks","display_name":"A General Multiple Data Augmentation Based Framework for Training Deep Neural Networks","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4312364074","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892528"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn55064.2022.9892528","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892528","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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 International Joint Conference on Neural Networks (IJCNN)","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/A5009957162","display_name":"Binyan Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I57093077","display_name":"Swinburne University of Technology","ror":"https://ror.org/031rekg67","country_code":"AU","type":"education","lineage":["https://openalex.org/I57093077"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Binyan Hu","raw_affiliation_strings":["Swinburne University of Technology,Department of Computing Technologies,Melbourne,Australia","Department of Computing Technologies, Swinburne University of Technology, Melbourne, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Swinburne University of Technology,Department of Computing Technologies,Melbourne,Australia","institution_ids":["https://openalex.org/I57093077"]},{"raw_affiliation_string":"Department of Computing Technologies, Swinburne University of Technology, Melbourne, Australia","institution_ids":["https://openalex.org/I57093077"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101870255","display_name":"Yu Sun","orcid":"https://orcid.org/0000-0002-0004-2863"},"institutions":[{"id":"https://openalex.org/I57093077","display_name":"Swinburne University of Technology","ror":"https://ror.org/031rekg67","country_code":"AU","type":"education","lineage":["https://openalex.org/I57093077"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yu Sun","raw_affiliation_strings":["Swinburne University of Technology,Department of Computing Technologies,Melbourne,Australia","Department of Computing Technologies, Swinburne University of Technology, Melbourne, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Swinburne University of Technology,Department of Computing Technologies,Melbourne,Australia","institution_ids":["https://openalex.org/I57093077"]},{"raw_affiliation_string":"Department of Computing Technologies, Swinburne University of Technology, Melbourne, Australia","institution_ids":["https://openalex.org/I57093077"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006614329","display_name":"A. K. Qin","orcid":"https://orcid.org/0000-0001-6631-1651"},"institutions":[{"id":"https://openalex.org/I57093077","display_name":"Swinburne University of Technology","ror":"https://ror.org/031rekg67","country_code":"AU","type":"education","lineage":["https://openalex.org/I57093077"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"A. K. Qin","raw_affiliation_strings":["Swinburne University of Technology,Department of Computing Technologies,Melbourne,Australia","Department of Computing Technologies, Swinburne University of Technology, Melbourne, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Swinburne University of Technology,Department of Computing Technologies,Melbourne,Australia","institution_ids":["https://openalex.org/I57093077"]},{"raw_affiliation_string":"Department of Computing Technologies, Swinburne University of Technology, Melbourne, Australia","institution_ids":["https://openalex.org/I57093077"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I57093077"],"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":"15","issue":null,"first_page":"01","last_page":"08"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9997000098228455,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9955999851226807,"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":0.8061450719833374},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.7471121549606323},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7002830505371094},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6197571754455566},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6168003678321838},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.6157936453819275},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.606205940246582},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5698717832565308}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8061450719833374},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.7471121549606323},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7002830505371094},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6197571754455566},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6168003678321838},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.6157936453819275},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.606205940246582},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5698717832565308}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn55064.2022.9892528","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892528","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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 International Joint Conference on Neural Networks (IJCNN)","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":56,"referenced_works":["https://openalex.org/W1416942850","https://openalex.org/W1821462560","https://openalex.org/W1965900320","https://openalex.org/W2066671570","https://openalex.org/W2095705004","https://openalex.org/W2108598243","https://openalex.org/W2194775991","https://openalex.org/W2302255633","https://openalex.org/W2321841392","https://openalex.org/W2549139847","https://openalex.org/W2620998106","https://openalex.org/W2746314669","https://openalex.org/W2765407302","https://openalex.org/W2802198257","https://openalex.org/W2803023299","https://openalex.org/W2806183416","https://openalex.org/W2807912816","https://openalex.org/W2904170036","https://openalex.org/W2945472816","https://openalex.org/W2949736877","https://openalex.org/W2963420686","https://openalex.org/W2963446712","https://openalex.org/W2963855133","https://openalex.org/W2964137095","https://openalex.org/W2987861506","https://openalex.org/W2992308087","https://openalex.org/W3034169498","https://openalex.org/W3034200289","https://openalex.org/W3034223443","https://openalex.org/W3035321581","https://openalex.org/W3035682985","https://openalex.org/W3114566572","https://openalex.org/W3118608800","https://openalex.org/W3138994021","https://openalex.org/W3163825800","https://openalex.org/W3168547821","https://openalex.org/W3183903096","https://openalex.org/W4286914341","https://openalex.org/W4288404646","https://openalex.org/W4295364049","https://openalex.org/W4297665946","https://openalex.org/W6638523607","https://openalex.org/W6674330103","https://openalex.org/W6713132643","https://openalex.org/W6728994542","https://openalex.org/W6743428213","https://openalex.org/W6745136726","https://openalex.org/W6751444130","https://openalex.org/W6751751081","https://openalex.org/W6752186649","https://openalex.org/W6752523896","https://openalex.org/W6757555829","https://openalex.org/W6763882748","https://openalex.org/W6775845032","https://openalex.org/W6779048707","https://openalex.org/W6791793911"],"related_works":["https://openalex.org/W2129767422","https://openalex.org/W3210196349","https://openalex.org/W4214728004","https://openalex.org/W2950181282","https://openalex.org/W2798287483","https://openalex.org/W2913410650","https://openalex.org/W4398789279","https://openalex.org/W2130553454","https://openalex.org/W3022007134","https://openalex.org/W4317548404"],"abstract_inverted_index":{"Deep":[0],"neural":[1],"networks":[2],"(DNNs)":[3],"often":[4],"rely":[5],"on":[6,81,281],"massive":[7],"labelled":[8,27,43,96],"data":[9,22,28,44,97,213],"for":[10,45,62,109,243,259,275],"training,":[11,64],"which":[12],"is":[13,196,241,257],"inaccessible":[14],"in":[15,164,181,188,225],"many":[16],"applications.":[17],"Data":[18],"augmentation":[19],"(DA)":[20],"tackles":[21],"scarcity":[23],"by":[24,100,215,266],"creating":[25],"new":[26],"from":[29,211,222],"available":[30],"ones.":[31],"Different":[32],"DA":[33,58,102,122,134,152,201,217,270],"methods":[34,103,115,218,271],"have":[35,85],"different":[36,54],"mechanisms":[37],"and":[38,185,202,219,228,272,298],"therefore":[39],"using":[40,267],"their":[41,182,189],"generated":[42,214],"DNN":[46,63,76,145,170],"training":[47,77,110,146,244,276,301],"may":[48],"help":[49],"improving":[50],"DNN's":[51,239],"generalisation":[52],"to":[53,68,93,149,175,294],"degrees.":[55],"Combining":[56],"multiple":[57,101,172,176],"methods,":[59,78,123],"namely":[60],"multi-DA,":[61],"provides":[65],"a":[66,141,156,161,199,203],"way":[67],"further":[69],"boost":[70],"generalisation.":[71],"Among":[72],"existing":[73,113,296],"multi-DA":[74,143,299],"based":[75,144,300],"those":[79],"relying":[80],"knowledge":[82,91],"distillation":[83],"(KD)":[84],"received":[86],"great":[87],"attention.":[88],"They":[89],"leverage":[90],"transfer":[92],"utilise":[94,118],"the":[95,130,165,169,212,220,235,245,250,253,263,282,289],"sets":[98],"created":[99],"instead":[104],"of":[105,121,125,129,132,168,193,237,249,285,291],"directly":[106],"combining":[107],"them":[108],"DNNs.":[111,278],"However,":[112],"KD-based":[114],"can":[116],"only":[117],"certain":[119,162],"types":[120],"incapable":[124],"making":[126],"full":[127],"use":[128,150],"advantages":[131],"arbitrary":[133,151],"methods.":[135,153,302],"In":[136],"this":[137],"work,":[138],"we":[139],"propose":[140],"general":[142],"framework":[147,159,265],"capable":[148],"To":[154],"train":[155],"DNN,":[157],"our":[158,292],"replicates":[160],"portion":[163],"latter":[166,190],"part":[167],"into":[171],"copies,":[173],"leading":[174],"DNNs":[177,195,224,251],"with":[178,198,252],"shared":[179],"blocks":[180,187],"former":[183],"parts":[184],"independent":[186],"parts.":[191],"Each":[192],"these":[194],"associated":[197],"unique":[200],"newly":[204],"devised":[205],"loss":[206],"that":[207],"allows":[208],"comprehensively":[209],"learning":[210],"all":[216,223],"outputs":[221],"an":[226],"online":[227],"adaptive":[229],"way.":[230],"The":[231],"overall":[232],"loss,":[233,240],"i.e.,":[234],"sum":[236],"each":[238],"used":[242],"DNN.":[246],"Eventually,":[247],"one":[248],"best":[254],"validation":[255],"performance":[256],"chosen":[258],"inference.":[260],"We":[261],"implement":[262],"proposed":[264],"three":[268],"distinct":[269],"apply":[273],"it":[274],"representative":[277],"Experimental":[279],"results":[280],"popular":[283],"benchmarks":[284],"image":[286],"classification":[287],"demonstrate":[288],"superiority":[290],"method":[293],"several":[295],"single-DA":[297]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
