AuthorsPérez, Juan C.
Thabet, Ali Kassem
KAUST DepartmentElectrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Visual Computing Center (VCC)
KAUST Grant NumberOSR-CRG2019-4033
Embargo End Date2021-11-05
Permanent link to this recordhttp://hdl.handle.net/10754/666421
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AbstractWe 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.
CitationPérez, 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
SponsorsThis 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.
Conference/Event name16th European Conference on Computer Vision, ECCV 2020