150 citations · 213 across the 12 of their papers we have counts for
12 papers
Let's Enhance: A Deep Learning Approach to Extreme Deblurring of Text Images
Theophil Trippe, Martin Genzel, Jan Macdonald +1
This work presents a novel deep-learning-based pipeline for the inverse problem of image deblurring, leveraging augmentation and pre-training with synthetic data. Our results build…
Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning
Martin Genzel, Ingo Gühring, Jan Macdonald +1
This work is concerned with the following fundamental question in scientific machine learning: Can deep-learning-based methods solve noise-free inverse problems to near-perfect acc…
A Complete Characterisation of ReLU-Invariant Distributions
Jan Macdonald, Stephan Wäldchen
We give a complete characterisation of families of probability distributions that are invariant under the action of ReLU neural network layers. The need for such families arises du…
Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings
Jan Macdonald, Mathieu Besançon, Sebastian Pokutta
We study the effects of constrained optimization formulations and Frank-Wolfe algorithms for obtaining interpretable neural network predictions. Reformulating the Rate-Distortion E…
AAPM DL-Sparse-View CT Challenge Submission Report: Designing an Iterative Network for Fanbeam-CT with Unknown Geometry
Martin Genzel, Jan Macdonald, Maximilian März
This report is dedicated to a short motivation and description of our contribution to the AAPM DL-Sparse-View CT Challenge (team name: "robust-and-stable"). The task is to recover…
Solving Inverse Problems With Deep Neural Networks -- Robustness Included?
Martin Genzel, Jan Macdonald, Maximilian März
In the past five years, deep learning methods have become state-of-the-art in solving various inverse problems. Before such approaches can find application in safety-critical field…