219 citations · 385 across the 13 of their papers we have counts for
14 papers
Patient Pose Assessment Using a CT-Based Framework for Synthetic Data Generation
Manuel Laufer, Dominik Mairhöfer, Malte Sieren +7
An adequate diagnostic quality of radiographs is essential for reliable diagnoses and treatment planning. The patient's pose during radiography is one of the most important factors…
Connections Between Pairs of Filters Improve the Accuracy of Convolutional Neural Networks
Kathleen Anderson, Philipp Grüning, Erhardt Barth
While researchers continue to find new and improved network structures for CNNs, most of the newly invented architectures still rely on the traditional pattern of stacking convolut…
Convolutional Neural Networks Do Work with Pre-Defined Filters
Christoph Linse, Erhardt Barth, Thomas Martinetz
We present a novel class of Convolutional Neural Networks called Pre-defined Filter Convolutional Neural Networks (PFCNNs), where all nxn convolution kernels with n>1 are pre-defin…
Leaky ReLUs That Differ in Forward and Backward Pass Facilitate Activation Maximization in Deep Neural Networks
Christoph Linse, Erhardt Barth, Thomas Martinetz
Activation maximization (AM) strives to generate optimal input stimuli, revealing features that trigger high responses in trained deep neural networks. AM is an important method of…
TEVR: Improving Speech Recognition by Token Entropy Variance Reduction
Hajo Nils Krabbenhöft, Erhardt Barth
This paper presents TEVR, a speech recognition model designed to minimize the variation in token entropy w.r.t. to the language model. This takes advantage of the fact that if the…
Bio-inspired Min-Nets Improve the Performance and Robustness of Deep Networks
Philipp Grüning, Erhardt Barth
Min-Nets are inspired by end-stopped cortical cells with units that output the minimum of two learned filters. We insert such Min-units into state-of-the-art deep networks, such as…