121 citations · 319 across the 7 of their papers we have counts for
8 papers
Learned convex regularizers for inverse problems
Subhadip Mukherjee, Sören Dittmer, Zakhar Shumaylov +3
We consider the variational reconstruction framework for inverse problems and propose to learn a data-adaptive input-convex neural network (ICNN) as the regularization functional.…
On Learned Operator Correction in Inverse Problems
Sebastian Lunz, Andreas Hauptmann, Tanja Tarvainen +2
We discuss the possibility to learn a data-driven explicit model correction for inverse problems and whether such a model correction can be used within a variational framework to o…
Inverse Graphics GAN: Learning to Generate 3D Shapes from Unstructured 2D Data
Sebastian Lunz, Yingzhen Li, Andrew Fitzgibbon +1
Recent work has shown the ability to learn generative models for 3D shapes from only unstructured 2D images. However, training such models requires differentiating through the rast…
On the Connection Between Adversarial Robustness and Saliency Map Interpretability
Christian Etmann, Sebastian Lunz, Peter Maass +1
Recent studies on the adversarial vulnerability of neural networks have shown that models trained to be more robust to adversarial attacks exhibit more interpretable saliency maps…
The Oracle of DLphi
Dominik Alfke, Weston Baines, Jan Blechschmidt +24
We present a novel technique based on deep learning and set theory which yields exceptional classification and prediction results. Having access to a sufficiently large amount of l…
Task adapted reconstruction for inverse problems
Jonas Adler, Sebastian Lunz, Olivier Verdier +2
The paper considers the problem of performing a task defined on a model parameter that is only observed indirectly through noisy data in an ill-posed inverse problem. A key aspect…
Banach Wasserstein GAN
Jonas Adler, Sebastian Lunz
Wasserstein Generative Adversarial Networks (WGANs) can be used to generate realistic samples from complicated image distributions. The Wasserstein metric used in WGANs is based on…
Adversarial Regularizers in Inverse Problems
Sebastian Lunz, Ozan Öktem, Carola-Bibiane Schönlieb
Inverse Problems in medical imaging and computer vision are traditionally solved using purely model-based methods. Among those variational regularization models are one of the most…