13 citations · 16 across the 6 of their papers we have counts for
4 papers · 1 filter
From a Fourier-Domain Perspective on Adversarial Examples to a Wiener Filter Defense for Semantic Segmentation
Nikhil Kapoor, Andreas Bär, Serin Varghese +4
Despite recent advancements, deep neural networks are not robust against adversarial perturbations. Many of the proposed adversarial defense approaches use computationally expensiv…
Transferable Universal Adversarial Perturbations Using Generative Models
Atiye Sadat Hashemi, Andreas Bär, Saeed Mozaffari +1
Deep neural networks tend to be vulnerable to adversarial perturbations, which by adding to a natural image can fool a respective model with high confidence. Recently, the existenc…
Class-Incremental Learning for Semantic Segmentation Re-Using Neither Old Data Nor Old Labels
Marvin Klingner, Andreas Bär, Philipp Donn +1
While neural networks trained for semantic segmentation are essential for perception in autonomous driving, most current algorithms assume a fixed number of classes, presenting a m…
Improved Noise and Attack Robustness for Semantic Segmentation by Using Multi-Task Training with Self-Supervised Depth Estimation
Marvin Klingner, Andreas Bär, Tim Fingscheidt
While current approaches for neural network training often aim at improving performance, less focus is put on training methods aiming at robustness towards varying noise conditions…