4 papers
Taming Nonconvexity with Epigraph Lifting
Clara Lage, Hok Shing Wong, Matthias Ehrhardt
We consider inverse problems regularized by a trained neural network with \emph{convex activations} and \emph{unrestricted weights}. The resulting optimization problem is generally…
Learning Regularization Functionals for Inverse Problems: A Comparative Study
Johannes Hertrich, Hok Shing Wong, Alexander Denker +16
In recent years, a variety of learned regularization frameworks for solving inverse problems in imaging have emerged. These offer flexible modeling together with mathematical insig…
An Adaptively Inexact Method for Bilevel Learning Using Primal-Dual Style Differentiation
Lea Bogensperger, Matthias J. Ehrhardt, Thomas Pock +2
We consider a bilevel learning framework for learning linear operators. In this framework, the learnable parameters are optimized via a loss function that also depends on the minim…
A primal-dual algorithm for image reconstruction with input-convex neural network regularizers
Matthias J. Ehrhardt, Subhadip Mukherjee, Hok Shing Wong
We address the optimization problem in a data-driven variational reconstruction framework, where the regularizer is parameterized by an input-convex neural network (ICNN). While gr…