219 citations · 225 across the 3 of their papers we have counts for
6 papers
Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning
Hammam Alshazly, Christoph Linse, Erhardt Barth +1
This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopt advanced d…
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…
Solving Raven's Progressive Matrices with Multi-Layer Relation Networks
Marius Jahrens, Thomas Martinetz
Raven's Progressive Matrices are a benchmark originally designed to test the cognitive abilities of humans. It has recently been adapted to test relational reasoning in machine lea…
Multi-layer Relation Networks
Marius Jahrens, Thomas Martinetz
Relational Networks (RN) as introduced by Santoro et al. (2017) have demonstrated strong relational reasoning capabilities with a rather shallow architecture. Its single-layer desi…
Adaptive Hierarchical Sensing for the Efficient Sampling of Sparse and Compressible Signals
Henry Schütze, Erhardt Barth, Thomas Martinetz
We present the novel adaptive hierarchical sensing algorithm K-AHS, which samples sparse or compressible signals with a measurement complexity equal to that of Compressed Sensing (…
Deep Convolutional Neural Networks as Generic Feature Extractors
Lars Hertel, Erhardt Barth, Thomas Käster +1
Recognizing objects in natural images is an intricate problem involving multiple conflicting objectives. Deep convolutional neural networks, trained on large datasets, achieve conv…