Clean Accuracy Degradation in Certified Lipschitz-Bounded Versus Adversarially Trained Vision Transformers Beyond 100M Parameters

Abstract

Abstract: Lipschitz bounded neural networks are certifiably robust and have a good trade-off between clean and certified accuracy. Existing Lipschitz bounding methods train from scratch and are limited to moderately sized networks (< 6M parameters). They require a fair amount of hyper-parameter tuning and are computationally prohibitive for large networks like Vision Transformers (5M to 660M parameters). Obtaining certified robustness of transformers is not feasible due to the non-scalability and inflexibility of the current methods. This work presents CertViT, a two-step proximal-projection method to a

Research Question

How does the clean accuracy degradation of certified Lipschitz-bounded Vision Transformers compare to adversarially trained ViTs when scaling beyond 100M parameters on ImageNet-1K?

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