3 papers
cs.LG2020
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…
cs.LG2020
Adversarial Robustness on In- and Out-Distribution Improves Explainability
Maximilian Augustin, Alexander Meinke, Matthias Hein
Neural networks have led to major improvements in image classification but suffer from being non-robust to adversarial changes, unreliable uncertainty estimates on out-distribution…
cs.LG2019
Towards neural networks that provably know when they don't know
Alexander Meinke, Matthias Hein
It has recently been shown that ReLU networks produce arbitrarily over-confident predictions far away from the training data. Thus, ReLU networks do not know when they don't know.…