38 citations · 50 across the 22 of their papers we have counts for
3 papers · 1 filter
MGiaD: Multigrid in all dimensions. Efficiency and robustness by coarsening in resolution and channel dimensions
Antonia van Betteray, Matthias Rottmann, Karsten Kahl
Current state-of-the-art deep neural networks for image classification are made up of 10 - 100 million learnable weights and are therefore inherently prone to overfitting. The comp…
Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning
Julian Burghoff, Robin Chan, Hanno Gottschalk +4
Bringing deep neural networks (DNNs) into safety critical applications such as automated driving, medical imaging and finance, requires a thorough treatment of the model's uncertai…
Detecting and Learning the Unknown in Semantic Segmentation
Robin Chan, Svenja Uhlemeyer, Matthias Rottmann +1
Semantic segmentation is a crucial component for perception in automated driving. Deep neural networks (DNNs) are commonly used for this task and they are usually trained on a clos…