51 citations · 338 across the 88 of their papers we have counts for
94 papers
ResBuilder: Automated Learning of Depth with Residual Structures
Julian Burghoff, Matthias Rottmann, Jill von Conta +3
In this work, we develop a neural architecture search algorithm, termed Resbuilder, that develops ResNet architectures from scratch that achieve high accuracy at moderate computati…
survAIval: Survival Analysis with the Eyes of AI
Kamil Kowol, Stefan Bracke, Hanno Gottschalk
In this study, we propose a novel approach to enrich the training data for automated driving by using a self-designed driving simulator and two human drivers to generate safety-cri…
Risk stratification of malignant melanoma using neural networks
Julian Burghoff, Leonhard Ackermann, Younes Salahdine +5
In order to improve the detection and classification of malignant melanoma, this paper describes an image-based method that can achieve AUROC values of up to 0.78 without additiona…
Detecting Novelties with Empty Classes
Svenja Uhlemeyer, Julian Lienen, Eyke Hüllermeier +1
For open world applications, deep neural networks (DNNs) need to be aware of previously unseen data and adaptable to evolving environments. Furthermore, it is desirable to detect a…
Augmentation-based Domain Generalization for Semantic Segmentation
Manuel Schwonberg, Fadoua El Bouazati, Nico M. Schmidt +1
Unsupervised Domain Adaptation (UDA) and domain generalization (DG) are two research areas that aim to tackle the lack of generalization of Deep Neural Networks (DNNs) towards unse…
Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving
Manuel Schwonberg, Joshua Niemeijer, Jan-Aike Termöhlen +4
Deep neural networks (DNNs) have proven their capabilities in many areas in the past years, such as robotics, or automated driving, enabling technological breakthroughs. DNNs play…