Search Results for author: Emanuel Laude

Found 7 papers, 2 papers with code

Adaptive proximal gradient methods are universal without approximation

no code implementations9 Feb 2024 Konstantinos A. Oikonomidis, Emanuel Laude, Puya Latafat, Andreas Themelis, Panagiotis Patrinos

We show that adaptive proximal gradient methods for convex problems are not restricted to traditional Lipschitzian assumptions.

Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields

no code implementations13 Jul 2021 Hartmut Bauermeister, Emanuel Laude, Thomas Möllenhoff, Michael Moeller, Daniel Cremers

In contrast to existing discretizations which suffer from a grid bias, we show that a piecewise polynomial discretization better preserves the continuous nature of our problem.

Stereo Matching

Bregman Proximal Framework for Deep Linear Neural Networks

no code implementations8 Oct 2019 Mahesh Chandra Mukkamala, Felix Westerkamp, Emanuel Laude, Daniel Cremers, Peter Ochs

This initiated the development of the Bregman proximal gradient (BPG) algorithm and an inertial variant (momentum based) CoCaIn BPG, which however rely on problem dependent Bregman distances.

Optimization of Inf-Convolution Regularized Nonconvex Composite Problems

no code implementations27 Mar 2019 Emanuel Laude, Tao Wu, Daniel Cremers

In this work, we consider nonconvex composite problems that involve inf-convolution with a Legendre function, which gives rise to an anisotropic generalization of the proximal mapping and Moreau-envelope.

Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies

1 code implementation7 Apr 2016 Emanuel Laude, Thomas Möllenhoff, Michael Moeller, Jan Lellmann, Daniel Cremers

Convex relaxations of nonconvex multilabel problems have been demonstrated to produce superior (provably optimal or near-optimal) solutions to a variety of classical computer vision problems.

Color Image Denoising Image Denoising +1

Sublabel-Accurate Relaxation of Nonconvex Energies

2 code implementations CVPR 2016 Thomas Möllenhoff, Emanuel Laude, Michael Moeller, Jan Lellmann, Daniel Cremers

We propose a novel spatially continuous framework for convex relaxations based on functional lifting.

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