{"id":"https://openalex.org/W4416251231","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228412","title":"Fast Adversarial Training with Neighborhood Diversity Alignment","display_name":"Fast Adversarial Training with Neighborhood Diversity Alignment","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251231","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228412"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228412","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228412","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5004142996","display_name":"Hongxin Zhi","orcid":"https://orcid.org/0009-0008-5235-7243"},"institutions":[{"id":"https://openalex.org/I169689159","display_name":"PLA Information Engineering University","ror":"https://ror.org/00mm1qk40","country_code":"CN","type":"education","lineage":["https://openalex.org/I169689159"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hongxin Zhi","raw_affiliation_strings":["Information Engineering University,Institute of Information Technology,Zhengzhou,China"],"affiliations":[{"raw_affiliation_string":"Information Engineering University,Institute of Information Technology,Zhengzhou,China","institution_ids":["https://openalex.org/I169689159"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040501978","display_name":"Hongtao Yu","orcid":"https://orcid.org/0000-0002-8836-337X"},"institutions":[{"id":"https://openalex.org/I169689159","display_name":"PLA Information Engineering University","ror":"https://ror.org/00mm1qk40","country_code":"CN","type":"education","lineage":["https://openalex.org/I169689159"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongtao Yu","raw_affiliation_strings":["Information Engineering University,Institute of Information Technology,Zhengzhou,China"],"affiliations":[{"raw_affiliation_string":"Information Engineering University,Institute of Information Technology,Zhengzhou,China","institution_ids":["https://openalex.org/I169689159"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002173873","display_name":"Shaomei Li","orcid":"https://orcid.org/0000-0002-8010-1850"},"institutions":[{"id":"https://openalex.org/I169689159","display_name":"PLA Information Engineering University","ror":"https://ror.org/00mm1qk40","country_code":"CN","type":"education","lineage":["https://openalex.org/I169689159"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaomei Li","raw_affiliation_strings":["Information Engineering University,Institute of Information Technology,Zhengzhou,China"],"affiliations":[{"raw_affiliation_string":"Information Engineering University,Institute of Information Technology,Zhengzhou,China","institution_ids":["https://openalex.org/I169689159"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5004142996"],"corresponding_institution_ids":["https://openalex.org/I169689159"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19455914,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9937999844551086,"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":0.9937999844551086,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.001500000013038516,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0007999999797903001,"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/adversarial-system","display_name":"Adversarial system","score":0.9142000079154968},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7592999935150146},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.5210999846458435},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4487999975681305},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.43720000982284546},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4187999963760376},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.3944999873638153},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.39250001311302185}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.9142000079154968},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7592999935150146},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6791999936103821},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5913000106811523},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5411999821662903},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.5210999846458435},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4487999975681305},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.43720000982284546},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4187999963760376},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3944999873638153},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.39250001311302185},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.35839998722076416},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.33820000290870667},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.32510000467300415},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3052999973297119},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3025999963283539},{"id":"https://openalex.org/C202269582","wikidata":"https://www.wikidata.org/wiki/Q2644277","display_name":"Complementarity (molecular biology)","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.2646999955177307},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C70136482","wikidata":"https://www.wikidata.org/wiki/Q13583781","display_name":"A-weighting","level":3,"score":0.25459998846054077},{"id":"https://openalex.org/C2778770139","wikidata":"https://www.wikidata.org/wiki/Q1966904","display_name":"Solver","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C193254401","wikidata":"https://www.wikidata.org/wiki/Q2160088","display_name":"Robust optimization","level":2,"score":0.2526000142097473},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228412","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228412","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":16,"referenced_works":["https://openalex.org/W1980287119","https://openalex.org/W2108598243","https://openalex.org/W2119112357","https://openalex.org/W2194775991","https://openalex.org/W2963857521","https://openalex.org/W2964054038","https://openalex.org/W2964137095","https://openalex.org/W3021182036","https://openalex.org/W3107235539","https://openalex.org/W4311086205","https://openalex.org/W4381186802","https://openalex.org/W4387965340","https://openalex.org/W4391611168","https://openalex.org/W4392091283","https://openalex.org/W4402675784","https://openalex.org/W4412808959"],"related_works":[],"abstract_inverted_index":{"Fast":[0,101],"adversarial":[1,55,88,142,148,163],"training":[2,56,113],"is":[3,153],"susceptible":[4],"to":[5,25,36,58,84,106,126,155,160],"a":[6,17,68,81,95,119,170],"failure":[7],"mode":[8],"known":[9],"as":[10],"catastrophic":[11,200],"overfitting":[12,201],"(CO),":[13],"which":[14,152],"can":[15],"cause":[16],"sudden":[18],"drop":[19],"in":[20],"the":[21,42,53,60,74,77,109,112,132,138,178,185,199,206],"model\u2019s":[22,43,179,186,207],"robust":[23],"accuracy":[24],"zero":[26],"against":[27],"multi-step":[28,54,87],"attacks.":[29],"Previous":[30],"research":[31],"has":[32,80],"proposed":[33,78],"various":[34],"approaches":[35,65],"mitigate":[37],"this":[38,91],"problem,":[39],"typically":[40],"comparing":[41],"state":[44],"before":[45],"and":[46,73,203],"after":[47],"CO":[48],"or":[49],"contrasting":[50],"it":[51],"with":[52],"process":[57],"identify":[59],"inducing":[61],"factors.":[62],"However,":[63],"these":[64],"analyze":[66],"only":[67],"limited":[69],"set":[70],"of":[71,76,86,134,140],"factors,":[72],"robustness":[75],"solutions":[79],"significant":[82],"gap":[83],"that":[85,195],"training.":[89,166],"In":[90],"paper,":[92],"we":[93,168],"propose":[94,146,169],"novel":[96,120],"framework":[97],"called":[98],"Neighborhood":[99],"Diversity-Aligned":[100],"Adversarial":[102],"Training":[103],"(NDA-FAT),":[104],"designed":[105],"systematically":[107],"address":[108],"issue":[110],"throughout":[111],"process.":[114],"NDA-FAT":[115,196],"commences":[116],"by":[117],"employing":[118],"neighborhood":[121],"diversity":[122,139],"random":[123],"initialization":[124],"strategy":[125],"generate":[127,161],"multiple":[128,191],"starting":[129,158],"points":[130,159],"within":[131],"vicinity":[133],"samples,":[135],"thereby":[136,182],"enhancing":[137,184],"subsequent":[141],"examples.":[143],"We":[144],"then":[145],"an":[147],"effectiveness":[149],"weighting":[150],"method,":[151],"used":[154],"select":[156],"high-quality":[157],"effective":[162],"examples":[164],"for":[165],"Moreover,":[167],"min-max":[171],"alignment":[172],"regularization":[173],"approach":[174],"aimed":[175],"at":[176],"improving":[177],"local":[180],"linearity,":[181],"further":[183],"robustness.":[187,208],"Evaluation":[188],"results":[189],"on":[190],"benchmark":[192],"datasets":[193],"demonstrate":[194],"effectively":[197],"mitigates":[198],"problem":[202],"significantly":[204],"improves":[205]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}
