{"id":"https://openalex.org/W3201345174","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534077","title":"Batch Normalization Assisted Adversarial Pruning: Towards Lightweight, Sparse and Robust Models","display_name":"Batch Normalization Assisted Adversarial Pruning: Towards Lightweight, Sparse and Robust Models","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3201345174","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534077","mag":"3201345174"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9534077","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534077","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 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/A5100692645","display_name":"Wei Xiao","orcid":"https://orcid.org/0000-0003-3449-1218"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]},{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiao Wei","raw_affiliation_strings":["Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101567069","display_name":"Yao Zhu","orcid":"https://orcid.org/0000-0003-0991-1970"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yao Zhu","raw_affiliation_strings":["Multimedia and Embedded System Lab, Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Multimedia and Embedded System Lab, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034104790","display_name":"Shu\u2010Tao Xia","orcid":"https://orcid.org/0000-0002-8639-982X"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shu-Tao Xia","raw_affiliation_strings":["PCL Research Center of Networks and Communications, Peng Cheng Laboratory, Shenzhen, China","Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"PCL Research Center of Networks and Communications, Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]},{"raw_affiliation_string":"Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100692645"],"corresponding_institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.13412109,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998999834060669,"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":0.9998999834060669,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9991000294685364,"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":0.9965000152587891,"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/computer-science","display_name":"Computer science","score":0.7874565720558167},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.7491042613983154},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.6907767057418823},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6314400434494019},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.5650118589401245},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49791908264160156},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.47241702675819397},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.4694860577583313},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.45586633682250977},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4339243173599243},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12267473340034485}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7874565720558167},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.7491042613983154},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.6907767057418823},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6314400434494019},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.5650118589401245},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49791908264160156},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.47241702675819397},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.4694860577583313},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.45586633682250977},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4339243173599243},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12267473340034485},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9534077","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534077","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.5099999904632568,"display_name":"Decent work and economic growth"}],"awards":[{"id":"https://openalex.org/G1548905363","display_name":null,"funder_award_id":"JCYJ20180508152204044","funder_id":"https://openalex.org/F4320327511","funder_display_name":"Development and Reform Commission of Shenzhen Municipality"},{"id":"https://openalex.org/G1729927818","display_name":null,"funder_award_id":"61771273","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320327511","display_name":"Development and Reform Commission of Shenzhen Municipality","ror":"https://ror.org/03jmg4515"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W1673923490","https://openalex.org/W1686810756","https://openalex.org/W1836465849","https://openalex.org/W1903029394","https://openalex.org/W1945616565","https://openalex.org/W2150066425","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2340897893","https://openalex.org/W2551176409","https://openalex.org/W2592929672","https://openalex.org/W2618530766","https://openalex.org/W2805003733","https://openalex.org/W2896457183","https://openalex.org/W2943235166","https://openalex.org/W2962835968","https://openalex.org/W2962851801","https://openalex.org/W2963207607","https://openalex.org/W2963341956","https://openalex.org/W2963384482","https://openalex.org/W2963389226","https://openalex.org/W2963403868","https://openalex.org/W2963674932","https://openalex.org/W2963813662","https://openalex.org/W2963828549","https://openalex.org/W2963881378","https://openalex.org/W2964137095","https://openalex.org/W2964153729","https://openalex.org/W2964233199","https://openalex.org/W2964253222","https://openalex.org/W2969985801","https://openalex.org/W2996074092","https://openalex.org/W3009751875","https://openalex.org/W3034175346","https://openalex.org/W3035081900","https://openalex.org/W3035743198","https://openalex.org/W3097573595","https://openalex.org/W3101114581","https://openalex.org/W3118608800","https://openalex.org/W4285719527","https://openalex.org/W4287864863","https://openalex.org/W4293846201","https://openalex.org/W4385245566","https://openalex.org/W6637162671","https://openalex.org/W6637373629","https://openalex.org/W6638667902","https://openalex.org/W6640425456","https://openalex.org/W6729756640","https://openalex.org/W6739868092","https://openalex.org/W6739901393","https://openalex.org/W6751979845","https://openalex.org/W6755207826","https://openalex.org/W6761937618","https://openalex.org/W6770551804","https://openalex.org/W6772453130"],"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/W2997056298","https://openalex.org/W4313346231","https://openalex.org/W2738001131","https://openalex.org/W4285785480"],"abstract_inverted_index":{"Although":[0],"deep":[1],"neural":[2],"networks":[3,129],"(DNNs)":[4],"have":[5],"achieved":[6],"great":[7],"success":[8],"in":[9,81,147,168],"many":[10],"areas,":[11],"they":[12],"are":[13],"extremely":[14],"vulnerable":[15],"to":[16,42,66,94,111,174],"imperceptible":[17],"adversarial":[18,25,77,169,176],"attacks.":[19],"DNNs":[20],"can":[21,103],"improve":[22],"robustness":[23],"through":[24],"training":[26],"at":[27],"the":[28,39,59,74,82,89,97,113,116,120,141,158,164,175],"expense":[29],"of":[30,61,85,99,115],"massive":[31],"computing":[32],"and":[33,45,63,70,144],"large":[34],"network":[35],"capacity,":[36],"which":[37,138],"makes":[38],"model":[40],"difficult":[41],"be":[43],"lightweight":[44,69],"deployed":[46],"on":[47,132],"edge":[48],"or":[49],"resource-constrained":[50],"devices.":[51],"To":[52],"solve":[53],"this":[54],"problem,":[55],"we":[56,87,109,134],"take":[57],"both":[58,140],"advantages":[60],"structured":[62],"unstructured":[64,148],"pruning":[65,121,152,170],"get":[67],"a":[68],"sparse":[71],"model.":[72],"With":[73],"fact":[75],"that":[76,96,127],"attacks":[78],"mainly":[79],"exist":[80],"high-frequency":[83,106],"component":[84],"images,":[86],"analyze":[88],"Batch":[90],"Normalization":[91],"(BN)":[92],"layer":[93],"find":[95],"magnitude":[98,114,146],"its":[100],"scaling":[101,117,142],"factor":[102,118,143],"identify":[104],"those":[105],"components.":[107],"Therefore,":[108],"propose":[110],"consider":[112],"for":[119],"criterion.":[122],"Furthermore,":[123],"unlike":[124],"existing":[125],"methods":[126,171],"prune":[128],"only":[130],"base":[131],"weights,":[133],"define":[135],"\u201ceffective":[136],"weight\u201d":[137],"considers":[139],"weight":[145],"pruning.":[149],"A":[150],"novel":[151],"method":[153],"is":[154],"proposed":[155],"by":[156],"considering":[157],"effective":[159],"weight.":[160],"We":[161],"also":[162],"verify":[163],"Lottery":[165],"Ticket":[166],"Hypothesis":[167],"yet":[172],"unknown":[173],"community.":[177]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}