Michael Moeller

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Technische Universität München

Professor for visual scene analysis at the University of Siegen

Generative Models for Generic Light Field Reconstruction

Recently deep generative models have achieved impressive progress in mod...
05/13/2020 ∙ by Paramanand Chandramouli, et al. ∙ 30 ∙ share

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Convolutional Simplex Projection Network (CSPN) for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation has been a subject of increased ...
07/24/2018 ∙ by Rania Briq, et al. ∙ 8 ∙ share

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Parametric Majorization for Data-Driven Energy Minimization Methods

Energy minimization methods are a classical tool in a multitude of compu...
08/17/2019 ∙ by Jonas Geiping, et al. ∙ 6 ∙ share

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Fast Convex Relaxations using Graph Discretizations

Matching and partitioning problems are fundamentals of computer vision a...
04/23/2020 ∙ by Jonas Geiping, et al. ∙ 1 ∙ share

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Tighter Lifting-Free Convex Relaxations for Quadratic Matching Problems

In this work we study convex relaxations of quadratic optimisation probl...
11/29/2017 ∙ by Florian Bernard, et al. ∙ 0 ∙ share

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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...
04/11/2017 ∙ by Tim Meinhardt, et al. ∙ 0 ∙ share

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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 ...
11/23/2016 ∙ by Jonas Geiping, et al. ∙ 0 ∙ share

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Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies

Convex relaxations of nonconvex multilabel problems have been demonstrat...
04/07/2016 ∙ by Emanuel Laude, et al. ∙ 0 ∙ share

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Sublabel-Accurate Relaxation of Nonconvex Energies

We propose a novel spatially continuous framework for convex relaxations...
12/04/2015 ∙ by Thomas Möllenhoff, et al. ∙ 0 ∙ share

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Nonlinear Spectral Analysis via One-homogeneous Functionals - Overview and Future Prospects

We present in this paper the motivation and theory of nonlinear spectral...
10/05/2015 ∙ by Guy Gilboa, et al. ∙ 0 ∙ share

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Point-wise Map Recovery and Refinement from Functional Correspondence

Since their introduction in the shape analysis community, functional map...
06/18/2015 ∙ by Emanuele Rodolà, et al. ∙ 0 ∙ share

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Variational Depth from Focus Reconstruction

This paper deals with the problem of reconstructing a depth map from a s...
08/01/2014 ∙ by Michael Moeller, et al. ∙ 0 ∙ share

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The Primal-Dual Hybrid Gradient Method for Semiconvex Splittings

This paper deals with the analysis of a recent reformulation of the prim...
07/07/2014 ∙ by Thomas Möllenhoff, et al. ∙ 0 ∙ share

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Composite Optimization by Nonconvex Majorization-Minimization

Many tasks in imaging can be modeled via the minimization of a nonconvex...
02/20/2018 ∙ by Jonas Geiping, et al. ∙ 0 ∙ share

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Lifting Layers: Analysis and Applications

The great advances of learning-based approaches in image processing and ...
03/23/2018 ∙ by Peter Ochs, et al. ∙ 0 ∙ share

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Are good local minima wide in sparse recovery?

The idea of compressed sensing is to exploit representations in suitable...
06/21/2018 ∙ by Michael Moeller, et al. ∙ 0 ∙ share

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Controlling Neural Networks via Energy Dissipation

The last decade has shown a tremendous success in solving various comput...
04/05/2019 ∙ by Michael Moeller, et al. ∙ 0 ∙ share

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Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory

We empirically evaluate common assumptions about neural networks that ar...
10/01/2019 ∙ by Micah Goldblum, et al. ∙ 0 ∙ share

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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...
03/31/2020 ∙ by Jonas Geiping, et al. ∙ 0 ∙ share

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Michael Moeller

Featured Co-authors

Daniel Cremers

Christian Theobalt

Tom Goldstein

Juergen Gall

Laura Leal-Taixé

Florian Bernard

Emanuele Rodolà

Martin Burger

Andreas Kolb

Jan Lellmann

Felix Krahmer

Generative Models for Generic Light Field Reconstruction

Convolutional Simplex Projection Network (CSPN) for Weakly Supervised Semantic Segmentation

Parametric Majorization for Data-Driven Energy Minimization Methods

Fast Convex Relaxations using Graph Discretizations

Tighter Lifting-Free Convex Relaxations for Quadratic Matching Problems

Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems

Multiframe Motion Coupling for Video Super Resolution

Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies

Sublabel-Accurate Relaxation of Nonconvex Energies

Nonlinear Spectral Analysis via One-homogeneous Functionals - Overview and Future Prospects

Point-wise Map Recovery and Refinement from Functional Correspondence

Variational Depth from Focus Reconstruction

The Primal-Dual Hybrid Gradient Method for Semiconvex Splittings

Composite Optimization by Nonconvex Majorization-Minimization

Lifting Layers: Analysis and Applications

Are good local minima wide in sparse recovery?

Controlling Neural Networks via Energy Dissipation

Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory

Inverting Gradients – How easy is it to break privacy in federated learning?