Bayesian view on the training of invertible residual networks for solving linear inverse problems

Clemens Arndt, Sören Dittmer, Nick Heilenkötter +3

Learning-based methods for inverse problems, adapting to the data's inherent structure, have become ubiquitous in the last decade. Besides empirical investigations of their often r…