598 citations · 2.9k across the 89 of their papers we have counts for
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On the Adversarial Robustness of Multi-Modal Foundation Models
Christian Schlarmann, Matthias Hein
Multi-modal foundation models combining vision and language models such as Flamingo or GPT-4 have recently gained enormous interest. Alignment of foundation models is used to preve…
Normalization Layers Are All That Sharpness-Aware Minimization Needs
Maximilian Mueller, Tiffany Vlaar, David Rolnick +1
Sharpness-aware minimization (SAM) was proposed to reduce sharpness of minima and has been shown to enhance generalization performance in various settings. In this work we show tha…
In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation
Julian Bitterwolf, Maximilian Müller, Matthias Hein
Out-of-distribution (OOD) detection is the problem of identifying inputs which are unrelated to the in-distribution task. The OOD detection performance when the in-distribution (ID…
A Modern Look at the Relationship between Sharpness and Generalization
Maksym Andriushchenko, Francesco Croce, Maximilian Müller +2
Sharpness of minima is a promising quantity that can correlate with generalization in deep networks and, when optimized during training, can improve generalization. However, standa…
Provably Adversarially Robust Nearest Prototype Classifiers
Václav Voráček, Matthias Hein
Nearest prototype classifiers (NPCs) assign to each input point the label of the nearest prototype with respect to a chosen distance metric. A direct advantage of NPCs is that the…
Sound Randomized Smoothing in Floating-Point Arithmetics
Václav Voráček, Matthias Hein
Randomized smoothing is sound when using infinite precision. However, we show that randomized smoothing is no longer sound for limited floating-point precision. We present a simple…