{"id":"https://openalex.org/W2909759446","doi":"https://doi.org/10.1145/3287624.3287645","title":"REIN","display_name":"REIN","publication_year":2019,"publication_date":"2019-01-18","ids":{"openalex":"https://openalex.org/W2909759446","doi":"https://doi.org/10.1145/3287624.3287645","mag":"2909759446"},"language":"en","primary_location":{"id":"doi:10.1145/3287624.3287645","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3287624.3287645","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th Asia and South Pacific Design Automation Conference","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/A5103085687","display_name":"Fuxun Yu","orcid":"https://orcid.org/0000-0002-4880-6658"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Fuxun Yu","raw_affiliation_strings":["George Mason University"],"affiliations":[{"raw_affiliation_string":"George Mason University","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100767202","display_name":"Chenchen Liu","orcid":"https://orcid.org/0000-0001-7749-0640"},"institutions":[{"id":"https://openalex.org/I16944753","display_name":"Clarkson University","ror":"https://ror.org/03rwgpn18","country_code":"US","type":"education","lineage":["https://openalex.org/I16944753"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chenchen Liu","raw_affiliation_strings":["Clarkson University"],"affiliations":[{"raw_affiliation_string":"Clarkson University","institution_ids":["https://openalex.org/I16944753"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100441944","display_name":"Xiang Chen","orcid":"https://orcid.org/0000-0002-8966-8159"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiang Chen","raw_affiliation_strings":["George Mason University"],"affiliations":[{"raw_affiliation_string":"George Mason University","institution_ids":["https://openalex.org/I162714631"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5103085687"],"corresponding_institution_ids":["https://openalex.org/I162714631"],"apc_list":null,"apc_paid":null,"fwci":0.14,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.53318501,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"456","last_page":"461"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998000264167786,"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":0.9998000264167786,"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":0.996999979019165,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.994700014591217,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.8334652185440063},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7875354290008545},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7531173229217529},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6423980593681335},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.47825658321380615},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.44937023520469666},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39681002497673035},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.06310617923736572}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.8334652185440063},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7875354290008545},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7531173229217529},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6423980593681335},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.47825658321380615},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.44937023520469666},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39681002497673035},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06310617923736572},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3287624.3287645","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3287624.3287645","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th Asia and South Pacific Design Automation Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.49000000953674316}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2006417020","https://openalex.org/W2085744236","https://openalex.org/W2117876524","https://openalex.org/W2125085157","https://openalex.org/W2129987407","https://openalex.org/W2136129837","https://openalex.org/W2144732271","https://openalex.org/W2154286738","https://openalex.org/W2271840356","https://openalex.org/W2346409908","https://openalex.org/W2460937040","https://openalex.org/W2490962171","https://openalex.org/W2516574342","https://openalex.org/W2593341394","https://openalex.org/W2612526924","https://openalex.org/W2615987742","https://openalex.org/W2753704268"],"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":[0,74],"recent":[1],"years,":[2],"neural":[3,18,35,60,85,102],"network":[4,103],"has":[5],"shown":[6],"its":[7],"great":[8],"potential":[9],"in":[10,89,121],"autonomous":[11,70,92],"driving":[12,71,93],"systems.":[13],"However,":[14],"the":[15,33,56,65,107],"theoretically":[16],"well-train":[17],"networks":[19,36,61],"usually":[20],"fail":[21],"their":[22],"performance":[23],"when":[24],"facing":[25],"real-world":[26],"examples":[27],"with":[28,132],"unexpected":[29,44],"physical":[30],"variations.":[31],"As":[32],"current":[34],"still":[37,139],"suffer":[38],"from":[39],"limited":[40],"generalization":[41,57,87],"ability,":[42],"those":[43],"variations":[45],"would":[46],"cause":[47],"considerable":[48],"accuracy":[49,116,144],"degradation":[50],"and":[51,101,128],"critical":[52,67],"safety":[53],"issues.":[54],"Therefore,":[55],"ability":[58,88,105],"of":[59,64],"becomes":[62],"one":[63],"most":[66,119,142],"challenges":[68],"for":[69],"system":[72],"design.":[73],"this":[75],"work,":[76],"we":[77],"propose":[78],"a":[79],"robust":[80],"training":[81,109],"method":[82,110],"to":[83],"enhance":[84],"network's":[86],"various":[90,122],"practical":[91,98],"scenarios.":[94],"Based":[95],"on":[96],"detailed":[97],"variation":[99],"modeling":[100],"generation":[104],"analysis,":[106],"proposed":[108],"could":[111,138],"consistently":[112],"improve":[113],"model":[114,137],"classification":[115],"by":[117],"at":[118,141],"25%":[120],"scenarios":[123],"(e.g.":[124],"raining/fogy,":[125],"dark":[126],"lighting,":[127],"camera":[129],"discrepancy).":[130],"Even":[131],"adversarial":[133],"corner":[134],"cases,":[135],"our":[136],"achieve":[140],"40%":[143],"improvement":[145],"over":[146],"natural":[147],"model.":[148]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2019-01-25T00:00:00"}