52 citations · 270 across the 25 of their papers we have counts for
4 papers · 1 filter
Proximal Residual Flows for Bayesian Inverse Problems
Johannes Hertrich
Normalizing flows are a powerful tool for generative modelling, density estimation and posterior reconstruction in Bayesian inverse problems. In this paper, we introduce proximal r…
Wasserstein Steepest Descent Flows of Discrepancies with Riesz Kernels
Johannes Hertrich, Manuel Gräf, Robert Beinert +1
The aim of this paper is twofold. Based on the geometric Wasserstein tangent space, we first introduce Wasserstein steepest descent flows. These are locally absolutely continuous c…
PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization
Fabian Altekrüger, Alexander Denker, Paul Hagemann +3
Learning neural networks using only few available information is an important ongoing research topic with tremendous potential for applications. In this paper, we introduce a power…
WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution
Fabian Altekrüger, Johannes Hertrich
Exploiting image patches instead of whole images have proved to be a powerful approach to tackle various problems in image processing. Recently, Wasserstein patch priors (WPP), whi…