15 citations · 29 across the 7 of their papers we have counts for
14 papers
Limited-Angle Tomography Reconstruction via Deep End-To-End Learning on Synthetic Data
Thomas Germer, Jan Robine, Sebastian Konietzny +2
Computed tomography (CT) has become an essential part of modern science and medicine. A CT scanner consists of an X-ray source that is spun around an object of interest. On the opp…
A Survey on Self-Supervised Representation Learning
Tobias Uelwer, Jan Robine, Stefan Sylvius Wagner +5
Learning meaningful representations is at the heart of many tasks in the field of modern machine learning. Recently, a lot of methods were introduced that allow learning of image r…
Transformer-based World Models Are Happy With 100k Interactions
Jan Robine, Marc Höftmann, Tobias Uelwer +1
Deep neural networks have been successful in many reinforcement learning settings. However, compared to human learners they are overly data hungry. To build a sample-efficient worl…
Deblurring Photographs of Characters Using Deep Neural Networks
Thomas Germer, Tobias Uelwer, Stefan Harmeling
In this paper, we present our approach for the Helsinki Deblur Challenge (HDC2021). The task of this challenge is to deblur images of characters without knowing the point spread fu…
Optimizing Intermediate Representations of Generative Models for Phase Retrieval
Tobias Uelwer, Sebastian Konietzny, Stefan Harmeling
Phase retrieval is the problem of reconstructing images from magnitude-only measurements. In many real-world applications the problem is underdetermined. When training data is avai…
A Closer Look at Reference Learning for Fourier Phase Retrieval
Tobias Uelwer, Nick Rucks, Stefan Harmeling
Reconstructing images from their Fourier magnitude measurements is a problem that often arises in different research areas. This process is also referred to as phase retrieval. In…