219 citations · 385 across the 13 of their papers we have counts for
9 papers · 1 filter
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…
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…
Feature Products Yield Efficient Networks
Philipp Grüning, Thomas Martinetz, Erhardt Barth
We introduce Feature-Product networks (FP-nets) as a novel deep-network architecture based on a new building block inspired by principles of biological vision. For each input featu…