From the 1 of 6 linked papers with an AI index.
6 papers
A neural operator view on U-Nets for inverse imaging problems
Alexander Auras, Martin Burger, Samira Kabri +2
Deep neural networks have shown great empirical success in the solution of a wide variety of ill-posed inverse problems in imaging. Yet, very few works have studied their behavior…
Allure of Craquelure: A Variational-Generative Approach to Crack Detection in Paintings
Laura Paul, Holger Rauhut, Martin Burger +2
The paper presents a hybrid method that treats crack detection in digitized paintings as an inverse problem, using a deep generative model to represent the crack-free artwork and a…
Position-Blind Ptychography: Viability of image reconstruction via data-driven variational inference
Simon Welker, Lorenz Kuger, Tim Roith +4
In this work, we present and investigate the novel blind inverse problem of position-blind ptychography, i.e., ptychographic phase retrieval without any knowledge of scan positions…
Explainable Learning Based Regularization of Inverse Problems
Martin Burger, Samira Kabri, Gitta Kutyniok +2
Machine learning techniques for the solution of inverse problems have become an attractive approach in the last decade, while their theoretical foundations are still in their infan…
Adversarial flows: A gradient flow characterization of adversarial attacks
Lukas Weigand, Tim Roith, Martin Burger
A popular method to perform adversarial attacks on neuronal networks is the so-called fast gradient sign method and its iterative variant. In this paper, we interpret this method a…
Analysis of mean-field models arising from self-attention dynamics in transformer architectures with layer normalization
Martin Burger, Samira Kabri, Yury Korolev +2
The aim of this paper is to provide a mathematical analysis of transformer architectures using a self-attention mechanism with layer normalization. In particular, observed patterns…