4 papers
Intriguing Properties of Robust Classification
Bernd Prach, Christoph H. Lampert
Despite extensive research since the community learned about adversarial examples 10 years ago, we still do not know how to train high-accuracy classifiers that are guaranteed to b…
1-Lipschitz Layers Compared: Memory, Speed, and Certifiable Robustness
Bernd Prach, Fabio Brau, Giorgio Buttazzo +1
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 syste…
1-Lipschitz Neural Networks are more expressive with N-Activations
Bernd Prach, Christoph H. Lampert
A crucial property for achieving secure, trustworthy and interpretable deep learning systems is their robustness: small changes to a system's inputs should not result in large chan…
Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks
Bernd Prach, Christoph H. Lampert
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 Lipschi…