← Search

Bernd Prach

2 accepted papers

2024

1-Lipschitz Layers Compared: Memory Speed and Certifiable Robustness

CVPR 2024poster

The robustness of neural networks against input perturbations with bounded magnitude represents a serious concern in the deployment of deep learning models in safety-critical systems. Recently the scientific community has focused on enhancing certifiable robustness guarantees by crafting \ols neural…

2022

Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks

ECCV 2022poster

"It is a highly desirable property for deep networks to be robust against small input changes. One popular way to achieve this property is by designing networks with a small Lipschitz constant. In this work, we propose a new technique for constructing such Lipschitz networks that has a number of des…