41 citations · 47 across the 7 of their papers we have counts for
9 papers
Few-Shot Learning for Personalised Automated Pain Assessment
Heinke Hihn, Ibrahim Eisawy, Patrick Thiam +2
Pain perception varies substantially across individuals, making it difficult for population-based classifiers to generalise across all subjects in a dataset. One way to account for…
Revisiting the Evaluation of Deep Neural Networks for Pedestrian Detection
Patrick Feifel, Benedikt Franke, Frank Bonarens +3
Reliable pedestrian detection represents a crucial step towards automated driving systems. However, the current performance benchmarks exhibit weaknesses. The currently applied met…
Trace and Detect Adversarial Attacks on CNNs using Feature Response Maps
Mohammadreza Amirian, Friedhelm Schwenker, Thilo Stadelmann
The existence of adversarial attacks on convolutional neural networks (CNN) questions the fitness of such models for serious applications. The attacks manipulate an input image suc…
Radial Basis Function Networks for Convolutional Neural Networks to Learn Similarity Distance Metric and Improve Interpretability
Mohammadreza Amirian, Friedhelm Schwenker
Radial basis function neural networks (RBFs) are prime candidates for pattern classification and regression and have been used extensively in classical machine learning application…
PrepNet: A Convolutional Auto-Encoder to Homogenize CT Scans for Cross-Dataset Medical Image Analysis
Mohammadreza Amirian, Javier A. Montoya-Zegarra, Jonathan Gruss +5
With the spread of COVID-19 over the world, the need arose for fast and precise automatic triage mechanisms to decelerate the spread of the disease by reducing human efforts e.g. f…
Binary Classification: Counterbalancing Class Imbalance by Applying Regression Models in Combination with One-Sided Label Shifts
Peter Bellmann, Heinke Hihn, Daniel A. Braun +1
In many real-world pattern recognition scenarios, such as in medical applications, the corresponding classification tasks can be of an imbalanced nature. In the current study, we f…
StrokeCoder: Path-Based Image Generation from Single Examples using Transformers
Sabine Wieluch, Friedhelm Schwenker
This paper demonstrates how a Transformer Neural Network can be used to learn a Generative Model from a single path-based example image. We further show how a data set can be gener…
Dropout Induced Noise for Co-Creative GAN Systems
Sabine Wieluch, Friedhelm Schwenker
This paper demonstrates how Dropout can be used in Generative Adversarial Networks to generate multiple different outputs to one input. This method is thought as an alternative to…
A dataset of continuous affect annotations and physiological signals for emotion analysis
Karan Sharma, Claudio Castellini, Egon L. van den Broek +2
From a computational viewpoint, emotions continue to be intriguingly hard to understand. In research, direct, real-time inspection in realistic settings is not possible. Discrete,…