Michael Moeller

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Technische Universität München

Professor for visual scene analysis at the University of Siegen

Nonlinear Spectral Geometry Processing via the TV Transform

We introduce a novel computational framework for digital geometry proces...
09/07/2020 ∙ by Marco Fumero, et al. ∙ 0 ∙ share

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Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching

Data Poisoning attacks involve an attacker modifying training data to ma...
09/04/2020 ∙ by Jonas Geiping, et al. ∙ 18 ∙ share

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Exploiting the Logits: Joint Sign Language Recognition and Spell-Correction

Machine learning techniques have excelled in the automatic semantic anal...
07/01/2020 ∙ by Christina Runkel, et al. ∙ 0 ∙ share

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A Simple Domain Shifting Networkfor Generating Low Quality Images

Deep Learning systems have proven to be extremely successful for image r...
06/30/2020 ∙ by Guruprasad Hegde, et al. ∙ 0 ∙ share

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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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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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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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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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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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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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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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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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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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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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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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Michael Moeller

Featured Co-authors

Daniel Cremers

Christian Theobalt

Tom Goldstein

Juergen Gall

Laura Leal-Taixé

Florian Bernard

Martin Burger

Emanuele Rodolà

Micah Goldblum

Felix Krahmer

Andreas Kolb

Nonlinear Spectral Geometry Processing via the TV Transform

Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching

Exploiting the Logits: Joint Sign Language Recognition and Spell-Correction

A Simple Domain Shifting Networkfor Generating Low Quality Images

Generative Models for Generic Light Field Reconstruction

Fast Convex Relaxations using Graph Discretizations

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

Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory

Parametric Majorization for Data-Driven Energy Minimization Methods

Controlling Neural Networks via Energy Dissipation

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

Are good local minima wide in sparse recovery?

Lifting Layers: Analysis and Applications

Composite Optimization by Nonconvex Majorization-Minimization

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