74 citations · 360 across the 51 of their papers we have counts for
11 papers · 1 filter
Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection
Tobias Riedlinger, Marius Schubert, Karsten Kahl +2
Active learning as a paradigm in deep learning is especially important in applications involving intricate perception tasks such as object detection where labels are difficult and…
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…
Semi-supervised domain adaptation with CycleGAN guided by a downstream task loss
Annika Mütze, Matthias Rottmann, Hanno Gottschalk
Domain adaptation is of huge interest as labeling is an expensive and error-prone task, especially when labels are needed on pixel-level like in semantic segmentation. Therefore, o…
Automated Detection of Label Errors in Semantic Segmentation Datasets via Deep Learning and Uncertainty Quantification
Matthias Rottmann, Marco Reese
In this work, we for the first time present a method for detecting label errors in image datasets with semantic segmentation, i.e., pixel-wise class labels. Annotation acquisition…
False Negative Reduction in Semantic Segmentation under Domain Shift using Depth Estimation
Kira Maag, Matthias Rottmann
State-of-the-art deep neural networks demonstrate outstanding performance in semantic segmentation. However, their performance is tied to the domain represented by the training dat…
What should AI see? Using the Public's Opinion to Determine the Perception of an AI
Robin Chan, Radin Dardashti, Meike Osinski +7
Deep neural networks (DNN) have made impressive progress in the interpretation of image data, so that it is conceivable and to some degree realistic to use them in safety critical…
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…
Coarsest-level improvements in multigrid for lattice QCD on large-scale computers
Jesus Espinoza-Valverde, Andreas Frommer, Gustavo Ramirez-Hidalgo +1
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 mat…
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…
UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs
Philipp Oberdiek, Gernot A. Fink, 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). Wh…
Towards Unsupervised Open World Semantic Segmentation
Svenja Uhlemeyer, Matthias Rottmann, Hanno Gottschalk
For the semantic segmentation of images, state-of-the-art deep neural networks (DNNs) achieve high segmentation accuracy if that task is restricted to a closed set of classes. Howe…