598 citations · 2.9k across the 89 of their papers we have counts for
11 papers · 1 filter
Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem
Matthias Hein, Maksym Andriushchenko, Julian Bitterwolf
Classifiers used in the wild, in particular for safety-critical systems, should not only have good generalization properties but also should know when they don't know, in particula…
Disentangling Adversarial Robustness and Generalization
David Stutz, Matthias Hein, Bernt Schiele
Obtaining deep networks that are robust against adversarial examples and generalize well is an open problem. A recent hypothesis even states that both robust and accurate models ar…
A randomized gradient-free attack on ReLU networks
Francesco Croce, Matthias Hein
It has recently been shown that neural networks but also other classifiers are vulnerable to so called adversarial attacks e.g. in object recognition an almost non-perceivable chan…
Logit Pairing Methods Can Fool Gradient-Based Attacks
Marius Mosbach, Maksym Andriushchenko, Thomas Trost +2
Recently, Kannan et al. [2018] proposed several logit regularization methods to improve the adversarial robustness of classifiers. We show that the computationally fast methods the…
Provable Robustness of ReLU networks via Maximization of Linear Regions
Francesco Croce, Maksym Andriushchenko, Matthias Hein
It has been shown that neural network classifiers are not robust. This raises concerns about their usage in safety-critical systems. We propose in this paper a regularization schem…
On the loss landscape of a class of deep neural networks with no bad local valleys
Quynh Nguyen, Mahesh Chandra Mukkamala, Matthias Hein
We identify a class of over-parameterized deep neural networks with standard activation functions and cross-entropy loss which provably have no bad local valley, in the sense that…