16 citations · 38 across the 6 of their papers we have counts for
6 papers
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…
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities
Julian Bitterwolf, Alexander Meinke, Maximilian Augustin +1
It is an important problem in trustworthy machine learning to recognize out-of-distribution (OOD) inputs which are inputs unrelated to the in-distribution task. Many out-of-distrib…
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…
Certifiably Adversarially Robust Detection of Out-of-Distribution Data
Julian Bitterwolf, Alexander Meinke, Matthias Hein
Deep neural networks are known to be overconfident when applied to out-of-distribution (OOD) inputs which clearly do not belong to any class. This is a problem in safety-critical a…
A simple way to make neural networks robust against diverse image corruptions
Evgenia Rusak, Lukas Schott, Roland S. Zimmermann +4
The human visual system is remarkably robust against a wide range of naturally occurring variations and corruptions like rain or snow. In contrast, the performance of modern image…
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…