6 papers
Self-Certifying Classification by Linearized Deep Assignment
Bastian Boll, Alexander Zeilmann, Stefania Petra +1
We propose a novel class of deep stochastic predictors for classifying metric data on graphs within the PAC-Bayes risk certification paradigm. Classifiers are realized as linearly…
Multi-Channel Potts-Based Reconstruction for Multi-Spectral Computed Tomography
Lukas Kiefer, Stefania Petra, Martin Storath +1
We consider reconstructing multi-channel images from measurements performed by photon-counting and energy-discriminating detectors in the setting of multi-spectral X-ray computed t…
Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least-Squares Case
Yair Censor, Stefania Petra, Christoph Schnörr
We conduct a study and comparison of superiorization and optimization approaches for the reconstruction problem of superiorized/regularized least-squares solutions of underdetermin…
Self-Assignment Flows for Unsupervised Data Labeling on Graphs
Matthias Zisler, Artjom Zern, Stefania Petra +1
This paper extends the recently introduced assignment flow approach for supervised image labeling to unsupervised scenarios where no labels are given. The resulting self-assignment…
Learning Adaptive Regularization for Image Labeling Using Geometric Assignment
Ruben Hühnerbein, Fabrizio Savarino, Stefania Petra +1
We study the inverse problem of model parameter learning for pixelwise image labeling, using the linear assignment flow and training data with ground truth. This is accomplished by…
Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment
Artjom Zern, Matthias Zisler, Stefania Petra +1
This paper introduces the unsupervised assignment flow that couples the assignment flow for supervised image labeling with Riemannian gradient flows for label evolution on feature…