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Tailored Uncertainty Estimation for Deep Learning Systems
Uncertainty estimation bears the potential to make deep learning (DL) systems more reliable.
Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis
We propose a novel framework consisting of a generative label-to-image synthesis model together with different transferability measures to inspect to what extent we can transfer testing results of semantic segmentation models from synthetic data to equivalent real-life data.
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
Our paper addresses both machine learning experts and safety engineers: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods.
Plants Don't Walk on the Street: Common-Sense Reasoning for Reliable Semantic Segmentation
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.
Street-Map Based Validation of Semantic Segmentation in Autonomous Driving
Lastly, we present quantitative results on the Cityscapes dataset indicating that our validation approach can indeed uncover errors in semantic segmentation masks.
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 adversarial perturbations.
Towards Map-Based Validation of Semantic Segmentation Masks
Artificial intelligence for autonomous driving must meet strict requirements on safety and robustness.
