196 citations · 203 across the 3 of their papers we have counts for
3 papers
cs.LG2022★ 2 cited
Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification
Leo Schwinn, Leon Bungert, An Nguyen +5
The reliability of neural networks is essential for their use in safety-critical applications. Existing approaches generally aim at improving the robustness of neural networks to e…
cs.LG2020★ 5 cited
Towards Rapid and Robust Adversarial Training with One-Step Attacks
Leo Schwinn, René Raab, Björn Eskofier
Adversarial training is the most successful empirical method for increasing the robustness of neural networks against adversarial attacks. However, the most effective approaches, l…
cs.LG2016★ 196 cited
Sensor-based Gait Parameter Extraction with Deep Convolutional Neural Networks
Julius Hannink, Thomas Kautz, Cristian F. Pasluosta +3
Measurement of stride-related, biomechanical parameters is the common rationale for objective gait impairment scoring. State-of-the-art double integration approaches to extract the…