In the life cycle of highly automated systems operating in an open and
d...
In this work, we develop a neural architecture search algorithm, termed
...
In this study, we propose a novel approach to enrich the training data f...
For open world applications, deep neural networks (DNNs) need to be awar...
Unsupervised Domain Adaptation (UDA) and domain generalization (DG) are ...
Deep neural networks (DNNs) have proven their capabilities in many areas...
The highly structured energy landscape of the loss as a function of
para...
Shape optimization with constraints given by partial differential equati...
LU-Net is a simple and fast architecture for invertible neural networks ...
Turbulent flow consists of structures with a wide range of spatial and
t...
This case-study aims at a comparison of the service quality of time-tabl...
Convolutional neural networks revolutionized computer vision and natrual...
Active learning as a paradigm in deep learning is especially important i...
In this work we present two video test data sets for the novel computer
...
Domain adaptation is of huge interest as labeling is an expensive and
er...
Deep neural networks (DNN) have made impressive progress in the
interpre...
Bringing deep neural networks (DNNs) into safety critical applications s...
The overall goal of this work is to enrich training data for automated
d...
Semantic segmentation is a crucial component for perception in automated...
For the semantic segmentation of images, state-of-the-art deep neural
ne...
While automated driving is often advertised with better-than-human drivi...
We present a mathematically well founded approach for the synthetic mode...
We present a novel post-processing tool for semantic segmentation of LiD...
To ensure safety in automated driving, the correct perception of the
sit...
Reliable epistemic uncertainty estimation is an essential component for
...
Instance segmentation with neural networks is an essential task in
envir...
Deep neural networks (DNNs) for the semantic segmentation of images are
...
In recent years, generative adversarial networks (GANs) have demonstrate...
In this work, we present an uncertainty-based method for sensor fusion w...
We present a novel region based active learning method for semantic imag...
Deep neural networks (DNNs) have proven to be powerful tools for process...
Multigrid methods have proven to be an invaluable tool to efficiently so...
We suggest a novel approach for the efficient and reliable approximation...
This paper describes the project GivEn that develops a novel multicriter...
In semantic segmentation datasets, classes of high importance are oftent...
In recent years, deep learning methods have outperformed other methods i...
In the semantic segmentation of street scenes, the reliability of a
pred...
Neural networks for semantic segmentation can be seen as statistical mod...
We consider the evaluation of manufacturing variations to the aerodynami...
As part of autonomous car driving systems, semantic segmentation is an
e...
We present a method that "meta" classifies whether segments (objects)
pr...
We study the quantification of uncertainty of Convolutional Neural Netwo...
In many applications the process of generating label information is expe...