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    ClusTR: Clustering Training for Robustness

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    Type
    Preprint
    Authors
    Alfarra, Motasem
    Pérez, Juan C.
    Bibi, Adel cc
    Thabet, Ali Kassem cc
    Arbeláez, Pablo
    Ghanem, Bernard cc
    KAUST Department
    Electrical Engineering
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Visual Computing Center (VCC)
    KAUST.
    Date
    2020-06-13
    Permanent link to this record
    http://hdl.handle.net/10754/666190
    
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    Abstract
    This paper studies how encouraging semantically-aligned features during deep neural network training can increase network robustness. Recent works observed that Adversarial Training leads to robust models, whose learnt features appear to correlate with human perception. Inspired by this connection from robustness to semantics, we study the complementary connection: from semantics to robustness. To do so, we provide a tight robustness certificate for distance-based classification models (clustering-based classifiers), which we leverage to propose ClusTR (Clustering Training for Robustness), a clustering-based and adversary-free training framework to learn robust models. Interestingly, ClusTR outperforms adversarially-trained networks by up to 4\% under strong PGD attacks. Moreover, it can be equipped with simple and fast adversarial training to improve the current state-of-the-art in robustness by 16\%-29\% on CIFAR10, SVHN, and CIFAR100.
    Publisher
    arXiv
    arXiv
    2006.07682
    Additional Links
    https://arxiv.org/pdf/2006.07682
    Collections
    Preprints; Electrical Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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