research
∙
03/02/2022
Detecting Adversarial Perturbations in Multi-Task Perception
While deep neural networks (DNNs) achieve impressive performance on envi...
share
research
∙
04/12/2021
Improving Online Performance Prediction for Semantic Segmentation
In this work we address the task of observing the performance of a seman...
research
∙
01/11/2021
The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing
Enabling autonomous driving (AD) can be considered one of the biggest ch...
research
∙
12/02/2020
From a Fourier-Domain Perspective on Adversarial Examples to a Wiener Filter Defense for Semantic Segmentation
Despite recent advancements, deep neural networks are not robust against...
research
∙
10/28/2020
Transferable Universal Adversarial Perturbations Using Generative Models
Deep neural networks tend to be vulnerable to adversarial perturbations,...
research
∙
05/12/2020
Class-Incremental Learning for Semantic Segmentation Re-Using Neither Old Data Nor Old Labels
While neural networks trained for semantic segmentation are essential fo...
research
∙
04/23/2020
Improved Noise and Attack Robustness for Semantic Segmentation by Using Multi-Task Training with Self-Supervised Depth Estimation
While current approaches for neural network training often aim at improv...
research
∙
02/25/2019
Towards Corner Case Detection for Autonomous Driving
The progress in autonomous driving is also due to the increased availabi...
research
∙
02/12/2019