Hanno Gottschalk

research

∙ 04/30/2023

Detecting Novelties with Empty Classes

For open world applications, deep neural networks (DNNs) need to be awar...

0 Svenja Uhlemeyer, et al. ∙

research

∙ 04/24/2023

Augmentation-based Domain Generalization for Semantic Segmentation

Unsupervised Domain Adaptation (UDA) and domain generalization (DG) are ...

0 Manuel Schwonberg, et al. ∙

research

∙ 04/24/2023

Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving

Deep neural networks (DNNs) have proven their capabilities in many areas...

0 Manuel Schwonberg, et al. ∙

research

∙ 04/14/2023

Who breaks early, looses: goal oriented training of deep neural networks based on port Hamiltonian dynamics

The highly structured energy landscape of the loss as a function of para...

0 Julian Burghoff, et al. ∙

research

∙ 02/28/2023

Non-convex shape optimization by dissipative Hamiltonian flows

Shape optimization with constraints given by partial differential equati...

0 Matthias Bolten, et al. ∙

research

∙ 02/21/2023

LU-Net: Invertible Neural Networks Based on Matrix Factorization

LU-Net is a simple and fast architecture for invertible neural networks ...

0 Robin Chan, et al. ∙

research

∙ 02/20/2023

Generalization capabilities of conditional GAN for turbulent flow under changes of geometry

Turbulent flow consists of structures with a wide range of spatial and t...

0 Claudia Drygala, et al. ∙

research

∙ 02/03/2023

Ridepooling and public bus services: A comparative case-study

This case-study aims at a comparison of the service quality of time-tabl...

0 Hayk Asatryan, et al. ∙

research

∙ 01/08/2023

Equivariant and Steerable Neural Networks: A review with special emphasis on the symmetric group

Convolutional neural networks revolutionized computer vision and natrual...

0 Patrick Krüger, et al. ∙

research

∙ 12/21/2022

Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection

Active learning as a paradigm in deep learning is especially important i...

0 Tobias Riedlinger, et al. ∙

research

∙ 10/05/2022

Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects

In this work we present two video test data sets for the novel computer ...

0 Kira Maag, et al. ∙

research

∙ 08/18/2022

Semi-supervised domain adaptation with CycleGAN guided by a downstream task loss

Domain adaptation is of huge interest as labeling is an expensive and er...

0 Annika Mütze, et al. ∙

research

∙ 06/09/2022

What should AI see? Using the Public's Opinion to Determine the Perception of an AI

Deep neural networks (DNN) have made impressive progress in the interpre...

0 Robin Chan, et al. ∙

research

∙ 05/30/2022

Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning

Bringing deep neural networks (DNNs) into safety critical applications s...

0 Julian Burghoff, et al. ∙

research

∙ 02/22/2022

A-Eye: Driving with the Eyes of AI for Corner Case Generation

The overall goal of this work is to enrich training data for automated d...

0 Kamil Kowol, et al. ∙

research

∙ 02/17/2022

Detecting and Learning the Unknown in Semantic Segmentation

Semantic segmentation is a crucial component for perception in automated...

4 Robin Chan, et al. ∙

research

∙ 01/04/2022

Towards Unsupervised Open World Semantic Segmentation

For the semantic segmentation of images, state-of-the-art deep neural ne...

31 Svenja Uhlemeyer, et al. ∙

research

∙ 12/09/2021

Does Redundancy in AI Perception Systems Help to Test for Super-Human Automated Driving Performance?

While automated driving is often advertised with better-than-human drivi...

0 Hanno Gottschalk, et al. ∙

research

∙ 12/05/2021

Generative Modeling of Turbulence

We present a mathematically well founded approach for the synthetic mode...

0 Claudia Drygala, et al. ∙

research

∙ 10/29/2021

False Positive Detection and Prediction Quality Estimation for LiDAR Point Cloud Segmentation

We present a novel post-processing tool for semantic segmentation of LiD...

0 Pascal Colling, et al. ∙

research

∙ 09/20/2021

Background-Foreground Segmentation for Interior Sensing in Automotive Industry

To ensure safety in automated driving, the correct perception of the sit...

9 Claudia Drygala, et al. ∙

research

∙ 07/09/2021

Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors

Reliable epistemic uncertainty estimation is an essential component for ...

0 Tobias Riedlinger, et al. ∙

research

∙ 12/14/2020

Improving Video Instance Segmentation by Light-weight Temporal Uncertainty Estimates

Instance segmentation with neural networks is an essential task in envir...

0 Kira Maag, et al. ∙

research

∙ 12/09/2020

Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic Segmentation

Deep neural networks (DNNs) for the semantic segmentation of images are ...

0 Robin Chan, et al. ∙

research

∙ 11/24/2020

A Convenient Infinite Dimensional Framework for Generative Adversarial Learning

In recent years, generative adversarial networks (GANs) have demonstrate...

0 Hayk Asatryan, et al. ∙

research

∙ 10/07/2020

YOdar: Uncertainty-based Sensor Fusion for Vehicle Detection with Camera and Radar Sensors

In this work, we present an uncertainty-based method for sensor fusion w...

0 Kamil Kowol, et al. ∙

research

∙ 10/05/2020

MetaBox+: A new Region Based Active Learning Method for Semantic Segmentation using Priority Maps

We present a novel region based active learning method for semantic imag...

0 Pascal Colling, et al. ∙

research

∙ 09/23/2020

Detection of Iterative Adversarial Attacks via Counter Attack

Deep neural networks (DNNs) have proven to be powerful tools for process...

0 Matthias Rottmann, et al. ∙

research

∙ 04/23/2020

Coarsening in Algebraic Multigrid using Gaussian Processes

Multigrid methods have proven to be an invaluable tool to efficiently so...

0 Hanno Gottschalk, et al. ∙

research

∙ 04/22/2020

Tracing locally Pareto optimal points by numerical integration

We suggest a novel approach for the efficient and reliable approximation...

0 Matthias Bolten, et al. ∙

research

∙ 02/20/2020

GivEn – Shape Optimization for Gas Turbines in Volatile Energy Networks

This paper describes the project GivEn that develops a novel multicriter...

0 Jan Backhaus, et al. ∙

research

∙ 12/16/2019

MetaFusion: Controlled False-Negative Reduction of Minority Classes in Semantic Segmentation

In semantic segmentation datasets, classes of high importance are oftent...

0 Robin Chan, et al. ∙

research

∙ 12/08/2019

Detection of False Positive and False Negative Samples in Semantic Segmentation

In recent years, deep learning methods have outperformed other methods i...

0 Matthias Rottmann, et al. ∙

research

∙ 11/12/2019

Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networks

In the semantic segmentation of street scenes, the reliability of a pred...

5 Kira Maag, et al. ∙

research

∙ 07/02/2019

The Ethical Dilemma when (not) Setting up Cost-based Decision Rules in Semantic Segmentation

Neural networks for semantic segmentation can be seen as statistical mod...

1 Robin Chan, et al. ∙

research

∙ 01/25/2019

Using adjoint CFD to quantify the impact of manufacturing variations on a heavy duty turbine vane

We consider the evaluation of manufacturing variations to the aerodynami...

0 Alexander Liefke, et al. ∙

research

∙ 01/24/2019

Application of Decision Rules for Handling Class Imbalance in Semantic Segmentation

As part of autonomous car driving systems, semantic segmentation is an e...

14 Robin Chan, et al. ∙

research

∙ 11/01/2018

Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities

We present a method that "meta" classifies whether segments (objects) pr...

6 Matthias Rottmann, et al. ∙

research

∙ 05/22/2018

Classification Uncertainty of Deep Neural Networks Based on Gradient Information

We study the quantification of uncertainty of Convolutional Neural Netwo...

0 Philipp Oberdiek, et al. ∙

research

∙ 03/03/2018

Deep Bayesian Active Semi-Supervised Learning

In many applications the process of generating label information is expe...

0 Matthias Rottmann, et al. ∙

Hanno Gottschalk

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