544 citations · 744 across the 12 of their papers we have counts for
13 papers
Certified Robust Models with Slack Control and Large Lipschitz Constants
Max Losch, David Stutz, Bernt Schiele +1
Despite recent success, state-of-the-art learning-based models remain highly vulnerable to input changes such as adversarial examples. In order to obtain certifiable robustness aga…
Robustifying Token Attention for Vision Transformers
Yong Guo, David Stutz, Bernt Schiele
Despite the success of vision transformers (ViTs), they still suffer from significant drops in accuracy in the presence of common corruptions, such as noise or blur. Interestingly,…
On Fragile Features and Batch Normalization in Adversarial Training
Nils Philipp Walter, David Stutz, Bernt Schiele
Modern deep learning architecture utilize batch normalization (BN) to stabilize training and improve accuracy. It has been shown that the BN layers alone are surprisingly expressiv…
Improving Robustness by Enhancing Weak Subnets
Yong Guo, David Stutz, Bernt Schiele
Despite their success, deep networks have been shown to be highly susceptible to perturbations, often causing significant drops in accuracy. In this paper, we investigate model rob…
A Closer Look at the Adversarial Robustness of Information Bottleneck Models
Iryna Korshunova, David Stutz, Alexander A. Alemi +2
We study the adversarial robustness of information bottleneck models for classification. Previous works showed that the robustness of models trained with information bottlenecks ca…
Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators
David Stutz, Nandhini Chandramoorthy, Matthias Hein +1
Deep neural network (DNN) accelerators received considerable attention in recent years due to the potential to save energy compared to mainstream hardware. Low-voltage operation of…