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Matthias Rottmann

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Related publications (4)

Page 1 of 1

Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors

Matthias Rottmann

The majority of uncertainty quantification methods for deep object detectors are based on the network output, such as sampling strategies like Monte-Carlo dropout or deep ensembles with straight-forward transfers to object detection. Here, we study gradien ...

IEEE Computer Soc2023

Coarsest-level improvements in multigrid for lattice QCD on large-scale computers

Matthias Rottmann

Numerical simulations of quantum chromodynamics (QCD) on a lattice require the frequent solution of linear systems of equations with large, sparse and typically ill-conditioned matrices. Algebraic multigrid methods are meanwhile the standard for these diff ...

ELSEVIER2023

Automated Detection of Label Errors in Semantic Segmentation Datasets via Deep Learning and Uncertainty Quantification

Matthias Rottmann

In this work, we present for the first time a method for detecting label errors in image datasets with semantic segmentation, i.e., pixel-wise class labels. Annotation acquisition for semantic segmentation datasets is time-consuming and requires plenty of ...

IEEE Computer Soc2023

UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs

Matthias Rottmann

We present an approach to quantifying both aleatoric and epistemic uncertainty for deep neural networks in image classification, based on generative adversarial networks (GANs). While most works in the literature that use GANs to generate out-of-distributi ...

2022