220 citations · 553 across the 12 of their papers we have counts for
15 papers
Bag of Tricks for Neural Architecture Search
Thomas Elsken, Benedikt Staffler, Arber Zela +2
While neural architecture search methods have been successful in previous years and led to new state-of-the-art performance on various problems, they have also been criticized for…
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…
Efficient Certified Defenses Against Patch Attacks on Image Classifiers
Jan Hendrik Metzen, Maksym Yatsura
Adversarial patches pose a realistic threat model for physical world attacks on autonomous systems via their perception component. Autonomous systems in safety-critical domains suc…
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…