598 citations · 2.9k across the 89 of their papers we have counts for
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Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks
Maksym Yatsura, Jan Hendrik Metzen, Matthias Hein
Adversarial attacks based on randomized search schemes have obtained state-of-the-art results in black-box robustness evaluation recently. However, as we demonstrate in this work,…
Being a Bit Frequentist Improves Bayesian Neural Networks
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Despite their compelling theoretical properties, Bayesian neural networks (BNNs) tend to perform worse than frequentist methods in classification-based uncertainty quantification (…
Provably Robust Detection of Out-of-distribution Data (almost) for free
Alexander Meinke, Julian Bitterwolf, Matthias Hein
The application of machine learning in safety-critical systems requires a reliable assessment of uncertainty. However, deep neural networks are known to produce highly overconfiden…
Adversarial Robustness against Multiple and Single -Threat Models via Quick Fine-Tuning of Robust Classifiers
Francesco Croce, Matthias Hein
A major drawback of adversarially robust models, in particular for large scale datasets like ImageNet, is the extremely long training time compared to standard ones. Moreover, mode…
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…
Relating Adversarially Robust Generalization to Flat Minima
David Stutz, Matthias Hein, Bernt Schiele
Adversarial training (AT) has become the de-facto standard to obtain models robust against adversarial examples. However, AT exhibits severe robust overfitting: cross-entropy loss…