49 citations · 157 across the 21 of their papers we have counts for
7 papers · 1 filter
Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis
Julia Rosenzweig, Eduardo Brito, Hans-Ulrich Kobialka +8
Many machine learning applications can benefit from simulated data for systematic validation - in particular if real-life data is difficult to obtain or annotate. However, since si…
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
Sebastian Houben, Stephanie Abrecht, Maram Akila +38
The use of deep neural networks (DNNs) in safety-critical applications like mobile health and autonomous driving is challenging due to numerous model-inherent shortcomings. These s…
Plants Don't Walk on the Street: Common-Sense Reasoning for Reliable Semantic Segmentation
Linara Adilova, Elena Schulz, Maram Akila +4
Data-driven sensor interpretation in autonomous driving can lead to highly implausible predictions as can most of the time be verified with common-sense knowledge. However, learnin…
Street-Map Based Validation of Semantic Segmentation in Autonomous Driving
Laura von Rueden, Tim Wirtz, Fabian Hueger +3
Artificial intelligence for autonomous driving must meet strict requirements on safety and robustness, which motivates the thorough validation of learned models. However, current v…
Supporting verification of news articles with automated search for semantically similar articles
Vishwani Gupta, Katharina Beckh, Sven Giesselbach +2
Fake information poses one of the major threats for society in the 21st century. Identifying misinformation has become a key challenge due to the amount of fake news that is publis…
Approaching Neural Network Uncertainty Realism
Joachim Sicking, Alexander Kister, Matthias Fahrland +5
Statistical models are inherently uncertain. Quantifying or at least upper-bounding their uncertainties is vital for safety-critical systems such as autonomous vehicles. While stan…
A Novel Regression Loss for Non-Parametric Uncertainty Optimization
Joachim Sicking, Maram Akila, Maximilian Pintz +3
Quantification of uncertainty is one of the most promising approaches to establish safe machine learning. Despite its importance, it is far from being generally solved, especially…