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Found 8 papers, 1 papers with code
Multi-level Geometric Optimization for Regularised Constrained Linear Inverse Problems
We present a geometric multilevel optimization approach that smoothly incorporates box constraints.
Self-Certifying Classification by Linearized Deep Assignment
We propose a novel class of deep stochastic predictors for classifying metric data on graphs within the PAC-Bayes risk certification paradigm.
Learning Linearized Assignment Flows for Image Labeling
An exact formula is derived for the parameter gradient of any loss function that is constrained by the linear system of ODEs determining the linearized assignment flow.
Multi-Channel Potts-Based Reconstruction for Multi-Spectral Computed Tomography
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 tomography (CT).
Self-Assignment Flows for Unsupervised Data Labeling on Graphs
This paper extends the recently introduced assignment flow approach for supervised image labeling to unsupervised scenarios where no labels are given.
Learning Adaptive Regularization for Image Labeling Using Geometric Assignment
We study the inverse problem of model parameter learning for pixelwise image labeling, using the linear assignment flow and training data with ground truth.
Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment
Experiments demonstrate a beneficial effect in both directions: adaptivity of labels improves image labeling, and steering label evolution by spatially regularized assignments leads to proper labels, because the assignment flow for supervised labeling is exactly used without any approximation for label learning.
