41 citations · 80 across the 6 of their papers we have counts for
6 papers
Anomaly-Aware Semantic Segmentation via Style-Aligned OoD Augmentation
Dan Zhang, Kaspar Sakmann, William Beluch +2
Within the context of autonomous driving, encountering unknown objects becomes inevitable during deployment in the open world. Therefore, it is crucial to equip standard semantic s…
Identification of Systematic Errors of Image Classifiers on Rare Subgroups
Jan Hendrik Metzen, Robin Hutmacher, N. Grace Hua +2
Despite excellent average-case performance of many image classifiers, their performance can substantially deteriorate on semantically coherent subgroups of the data that were under…
Test-Time Adaptation to Distribution Shift by Confidence Maximization and Input Transformation
Chaithanya Kumar Mummadi, Robin Hutmacher, Kilian Rambach +3
Deep neural networks often exhibit poor performance on data that is unlikely under the train-time data distribution, for instance data affected by corruptions. Previous works demon…
Does enhanced shape bias improve neural network robustness to common corruptions?
Chaithanya Kumar Mummadi, Ranjitha Subramaniam, Robin Hutmacher +3
Convolutional neural networks (CNNs) learn to extract representations of complex features, such as object shapes and textures to solve image recognition tasks. Recent work indicate…
Meta Adversarial Training against Universal Patches
Jan Hendrik Metzen, Nicole Finnie, Robin Hutmacher
Recently demonstrated physical-world adversarial attacks have exposed vulnerabilities in perception systems that pose severe risks for safety-critical applications such as autonomo…
Increasing the Robustness of Semantic Segmentation Models with Painting-by-Numbers
Christoph Kamann, Burkhard Güssefeld, Robin Hutmacher +2
For safety-critical applications such as autonomous driving, CNNs have to be robust with respect to unavoidable image corruptions, such as image noise. While previous works address…