Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem. - Matthias Hein 001; Maksym Andriushchenko; Julian Bitterwolf (CVPR, 2019)

A Simple Way to Make Neural Networks Robust Against Diverse Image Corruptions. - Evgenia Rusak; Lukas Schott; Roland S. Zimmermann; Julian Bitterwolf; Oliver Bringmann 001; Matthias Bethge; Wieland Brendel (ECCV (3), 2020)

Certifiably Adversarially Robust Detection of Out-of-Distribution Data. - Julian Bitterwolf; Alexander Meinke; Matthias Hein 001 (NeurIPS, 2020)

Increasing the robustness of DNNs against image corruptions by playing the Game of Noise. - Evgenia Rusak; Lukas Schott; Roland S. Zimmermann; Julian Bitterwolf; Oliver Bringmann 001; Matthias Bethge; Wieland Brendel (CoRR, 2020)

Provable Worst Case Guarantees for the Detection of Out-of-Distribution Data. - Julian Bitterwolf; Alexander Meinke; Matthias Hein 001 (CoRR, 2020)

Provably Robust Detection of Out-of-distribution Data (almost) for free. - Alexander Meinke; Julian Bitterwolf; Matthias Hein 001 (CoRR, 2021)

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; Matthias Hein 001 (ICML, 2022)

Provably Adversarially Robust Detection of Out-of-Distribution Data (Almost) for Free. - Alexander Meinke; Julian Bitterwolf; Matthias Hein 001 (NeurIPS, 2022)

Neural Network Heuristic Functions: Taking Confidence into Account. - Daniel Heller; Patrick Ferber; Julian Bitterwolf; Matthias Hein 001; Jörg Hoffmann 001 (SOCS, 2022)

In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation. - Julian Bitterwolf; Maximilian Müller; Matthias Hein 001 (ICML, 2023)