
Generative Models for Generic Light Field Reconstruction
Recently deep generative models have achieved impressive progress in mod...
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Convolutional Simplex Projection Network (CSPN) for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation has been a subject of increased ...
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Parametric Majorization for DataDriven Energy Minimization Methods
Energy minimization methods are a classical tool in a multitude of compu...
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Fast Convex Relaxations using Graph Discretizations
Matching and partitioning problems are fundamentals of computer vision a...
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Tighter LiftingFree Convex Relaxations for Quadratic Matching Problems
In this work we study convex relaxations of quadratic optimisation probl...
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Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems
While variational methods have been among the most powerful tools for so...
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Multiframe Motion Coupling for Video Super Resolution
The idea of video super resolution is to use different view points of a ...
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SublabelAccurate Convex Relaxation of Vectorial Multilabel Energies
Convex relaxations of nonconvex multilabel problems have been demonstrat...
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SublabelAccurate Relaxation of Nonconvex Energies
We propose a novel spatially continuous framework for convex relaxations...
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Nonlinear Spectral Analysis via Onehomogeneous Functionals  Overview and Future Prospects
We present in this paper the motivation and theory of nonlinear spectral...
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Pointwise Map Recovery and Refinement from Functional Correspondence
Since their introduction in the shape analysis community, functional map...
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Variational Depth from Focus Reconstruction
This paper deals with the problem of reconstructing a depth map from a s...
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The PrimalDual Hybrid Gradient Method for Semiconvex Splittings
This paper deals with the analysis of a recent reformulation of the prim...
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Composite Optimization by Nonconvex MajorizationMinimization
Many tasks in imaging can be modeled via the minimization of a nonconvex...
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Lifting Layers: Analysis and Applications
The great advances of learningbased approaches in image processing and ...
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Are good local minima wide in sparse recovery?
The idea of compressed sensing is to exploit representations in suitable...
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Controlling Neural Networks via Energy Dissipation
The last decade has shown a tremendous success in solving various comput...
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Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory
We empirically evaluate common assumptions about neural networks that ar...
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Inverting Gradients – How easy is it to break privacy in federated learning?
The idea of federated learning is to collaboratively train a neural netw...
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Michael Moeller
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Professor for visual scene analysis at the University of Siegen