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Improving the adversarial robustness of quantized neural networks via exploiting the feature diversity.

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Title: Improving the adversarial robustness of quantized neural networks via exploiting the feature diversity.
Authors: Chu, Tianshu1, chutianshu@sjtu.edu.cn, Fang, Kun1, fanghenshao@sjtu.edu.cn, Yang, Jie2, jieyang@sjtu.edu.cn, Huang, Xiaolin1,2, xiaolinhuang@sjtu.edu.cn
Source: Pattern Recognition Letters; Dec2023, Vol. 176, p117-122, 6p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 174013955
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=174013955
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.patrec.2023.10.024
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 6
        StartPage: 117
    Titles:
      – TitleFull: Improving the adversarial robustness of quantized neural networks via exploiting the feature diversity.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Chu, Tianshu
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          Name:
            NameFull: Fang, Kun
      – PersonEntity:
          Name:
            NameFull: Yang, Jie
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            NameFull: Huang, Xiaolin
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          Dates:
            – D: 01
              M: 12
              Text: Dec2023
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-print
              Value: 01678655
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            – Type: volume
              Value: 176
          Titles:
            – TitleFull: Pattern Recognition Letters
              Type: main
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