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    Gabor Layers Enhance Network Robustness

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    Thumbnail
    Name:
    123540426.pdf
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    1.981Mb
    Format:
    PDF
    Description:
    Accepted Manuscript
    Embargo End Date:
    2021-11-05
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    Type
    Conference Paper
    Authors
    Pérez, Juan C.
    Alfarra, Motasem
    Jeanneret, Guillaume
    Bibi, Adel cc
    Thabet, Ali Kassem cc
    Ghanem, Bernard cc
    Arbeláez, Pablo
    KAUST Department
    Electrical Engineering
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Visual Computing Center (VCC)
    KAUST Grant Number
    OSR-CRG2019-4033
    Date
    2020-11-05
    Embargo End Date
    2021-11-05
    Permanent link to this record
    http://hdl.handle.net/10754/666421
    
    Metadata
    Show full item record
    Abstract
    We revisit the benefits of merging classical vision concepts with deep learning models. In particular, we explore the effect of replacing the first layers of various deep architectures with Gabor layers (i.e. convolutional layers with filters that are based on learnable Gabor parameters) on robustness against adversarial attacks. We observe that architectures with Gabor layers gain a consistent boost in robustness over regular models and maintain high generalizing test performance. We then exploit the analytical expression of Gabor filters to derive a compact expression for a Lipschitz constant of such filters, and harness this theoretical result to develop a regularizer we use during training to further enhance network robustness. We conduct extensive experiments with various architectures (LeNet, AlexNet, VGG16, and WideResNet) on several datasets (MNIST, SVHN, CIFAR10 and CIFAR100) and demonstrate large empirical robustness gains. Furthermore, we experimentally show how our regularizer provides consistent robustness improvements.
    Citation
    Pérez, J. C., Alfarra, M., Jeanneret, G., Bibi, A., Thabet, A., Ghanem, B., & Arbeláez, P. (2020). Gabor Layers Enhance Network Robustness. Lecture Notes in Computer Science, 450–466. doi:10.1007/978-3-030-58545-7_26
    Sponsors
    This work was partially supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-CRG2019-4033.
    Publisher
    Springer International Publishing
    Conference/Event name
    16th European Conference on Computer Vision, ECCV 2020
    ISBN
    9783030585440
    DOI
    10.1007/978-3-030-58545-7_26
    arXiv
    1912.05661
    Additional Links
    http://link.springer.com/10.1007/978-3-030-58545-7_26
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-030-58545-7_26
    Scopus Count
    Collections
    Conference Papers; Electrical Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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