Abstract
We revisit the benefits of merging classical vision concepts with deep learning models. In particular, we explore the effect on robustness against adversarial attacks 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. We observe that architectures enhanced with Gabor layers gain a consistent boost in robustness over regular models and preserve high generalizing test performance, even though these layers come at a negligible increase in the number of parameters. We then exploit the closed form expression of Gabor filters to derive an 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.
Motasem Alfarra
Machine Learning Researcher at Qualcomm AI Research, Amsterdam, Netherlands
I am a machine learning researcher at Qualcomm AI Research in Amsterdam, Netherlands. I obtained my Ph.D. in Electrical and Computer Engineering from KAUST in Saudi Arabia advised by Prof. Bernard Ghanem. I also obtained my M.Sc degree in Electrical Engineering from KAUST, and my undergraduate degree in Electrical Engineering from Kuwait University. I am interested in domain shifts, LLM safety, and how to combat them with test-time adaptation and continual learning. I helped co-organizing the first workshop on Test-Time Adaptation at CVPR2024!