{"id":"https://openalex.org/W3081704442","doi":"https://doi.org/10.1109/icaci49185.2020.9177827","title":"Improving Robustness of Deep Transfer Model by Double Transfer Learning","display_name":"Improving Robustness of Deep Transfer Model by Double Transfer Learning","publication_year":2020,"publication_date":"2020-08-01","ids":{"openalex":"https://openalex.org/W3081704442","doi":"https://doi.org/10.1109/icaci49185.2020.9177827","mag":"3081704442"},"language":"en","primary_location":{"id":"doi:10.1109/icaci49185.2020.9177827","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaci49185.2020.9177827","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 12th International Conference on Advanced Computational Intelligence (ICACI)","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/A5100979920","display_name":"Lin Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lin Yu","raw_affiliation_strings":["School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036953616","display_name":"Xingda Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingda Wang","raw_affiliation_strings":["School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100459915","display_name":"Xiaoping Wang","orcid":"https://orcid.org/0000-0002-4909-8286"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoping Wang","raw_affiliation_strings":["School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081245089","display_name":"Zhigang Zeng","orcid":"https://orcid.org/0000-0003-4587-3588"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhigang Zeng","raw_affiliation_strings":["School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100979920"],"corresponding_institution_ids":["https://openalex.org/I47720641"],"apc_list":null,"apc_paid":null,"fwci":0.1326,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.54240816,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"17","issue":null,"first_page":"356","last_page":"363"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.980400025844574,"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/T11242","display_name":"Nuclear Materials and Properties","score":0.9455000162124634,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.8340449333190918},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.8174512982368469},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7991023063659668},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7952537536621094},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7636085748672485},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7618147134780884},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5595046281814575},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.468539834022522},{"id":"https://openalex.org/keywords/intuition","display_name":"Intuition","score":0.43321675062179565},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4297247529029846}],"concepts":[{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.8340449333190918},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8174512982368469},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7991023063659668},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7952537536621094},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7636085748672485},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7618147134780884},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5595046281814575},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.468539834022522},{"id":"https://openalex.org/C132010649","wikidata":"https://www.wikidata.org/wiki/Q189222","display_name":"Intuition","level":2,"score":0.43321675062179565},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4297247529029846},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icaci49185.2020.9177827","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaci49185.2020.9177827","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 12th International Conference on Advanced Computational Intelligence (ICACI)","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":59,"referenced_works":["https://openalex.org/W1595917421","https://openalex.org/W1673923490","https://openalex.org/W1731081199","https://openalex.org/W1945616565","https://openalex.org/W2096943734","https://openalex.org/W2099471712","https://openalex.org/W2112796928","https://openalex.org/W2120708938","https://openalex.org/W2149466042","https://openalex.org/W2149933564","https://openalex.org/W2159291411","https://openalex.org/W2161381512","https://openalex.org/W2164943005","https://openalex.org/W2165698076","https://openalex.org/W2180612164","https://openalex.org/W2279034837","https://openalex.org/W2460937040","https://openalex.org/W2511131004","https://openalex.org/W2593768305","https://openalex.org/W2618043096","https://openalex.org/W2620038827","https://openalex.org/W2743316710","https://openalex.org/W2774644650","https://openalex.org/W2788768841","https://openalex.org/W2795155917","https://openalex.org/W2894012075","https://openalex.org/W2904549000","https://openalex.org/W2951670162","https://openalex.org/W2953127297","https://openalex.org/W2963178695","https://openalex.org/W2963207607","https://openalex.org/W2963275094","https://openalex.org/W2963597678","https://openalex.org/W2963744840","https://openalex.org/W2963857521","https://openalex.org/W2964109570","https://openalex.org/W2964153729","https://openalex.org/W2964253222","https://openalex.org/W2964278684","https://openalex.org/W2964288524","https://openalex.org/W4293846201","https://openalex.org/W4299518610","https://openalex.org/W4300511536","https://openalex.org/W4320013936","https://openalex.org/W6637162671","https://openalex.org/W6637618735","https://openalex.org/W6640425456","https://openalex.org/W6681637710","https://openalex.org/W6682132143","https://openalex.org/W6683619738","https://openalex.org/W6685736903","https://openalex.org/W6695692224","https://openalex.org/W6713955831","https://openalex.org/W6719080892","https://openalex.org/W6725448924","https://openalex.org/W6726114608","https://openalex.org/W6739868092","https://openalex.org/W6750109254","https://openalex.org/W6755131061"],"related_works":["https://openalex.org/W2950183588","https://openalex.org/W3080754722","https://openalex.org/W4383221314","https://openalex.org/W3093978547","https://openalex.org/W2953536436","https://openalex.org/W3203790781","https://openalex.org/W4313346231","https://openalex.org/W2738001131","https://openalex.org/W4285785480","https://openalex.org/W2997056298"],"abstract_inverted_index":{"In":[0,77],"recent":[1],"years,":[2],"deep":[3,15,46,71,97,142,190],"learning":[4,12,42,59,99,128],"models":[5,43,73,144],"have":[6],"been":[7],"widely":[8],"adopted":[9],"for":[10],"transfer":[11,41,58,72,98,127,143,191],"tasks.":[13],"The":[14],"models,":[16],"however,":[17],"were":[18],"shown":[19],"to":[20,66,92,114,130,167],"be":[21],"easily":[22],"attacked":[23],"by":[24],"adversarial":[25,64,117,151,163],"examples,":[26],"which":[27],"is":[28,74,103],"generated":[29],"from":[30,153],"original":[31],"samples":[32],"with":[33,87,116],"the":[34,40,55,62,68,94,105,108,120,132,139,150,169,186,189],"carefully":[35],"designed":[36],"small":[37],"perturbations.":[38],"Thus,":[39],"based":[44],"on":[45,172,193],"networks":[47],"will":[48],"also":[49],"face":[50],"this":[51,78],"problem.":[52],"Because":[53],"of":[54,57,70,96,107,134,141,145,156,188],"particularity":[56],"tasks,":[60],"using":[61,104],"conventional":[63],"training":[65],"improve":[67,138,168],"robustness":[69,95,187],"very":[75],"difficult.":[76],"paper,":[79],"we":[80,123],"propose":[81],"a":[82,125],"novel":[83],"Double":[84],"Transfer":[85],"Learning":[86],"Adversarial":[88],"Training":[89],"(DTLAT)":[90],"method":[91,129],"enhance":[93],"models.":[100],"Our":[101],"intuition":[102],"instances":[106],"source":[109],"domain":[110,113,164],"and":[111,137],"target":[112,146],"help":[115],"training.":[118],"At":[119],"same":[121],"time,":[122],"design":[124],"reverse":[126],"weaken":[131],"effect":[133],"attack":[135,157],"methods":[136,183],"performance":[140],"domain.":[147],"We":[148],"regard":[149],"examples":[152],"some":[154],"kind":[155],"method,":[158],"like":[159],"FGSM,":[160],"as":[161],"an":[162],"while":[165],"try":[166],"generalization":[170],"ability":[171],"other":[173,182],"attacks":[174],"method.":[175],"Experiments":[176],"demonstrate":[177],"that":[178],"DTLAT":[179],"exceed":[180],"many":[181],"about":[184],"improving":[185],"model":[192],"several":[194],"benchmark":[195],"datasets.":[196]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}