169 citations · 422 across the 13 of their papers we have counts for
23 papers
Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields
Hartmut Bauermeister, Emanuel Laude, Thomas Möllenhoff +2
Dual decomposition approaches in nonconvex optimization may suffer from a duality gap. This poses a challenge when applying them directly to nonconvex problems such as MAP-inferenc…
Learning to Identify Physical Parameters from Video Using Differentiable Physics
Rama Krishna Kandukuri, Jan Achterhold, Michael Möller +1
Video representation learning has recently attracted attention in computer vision due to its applications for activity and scene forecasting or vision-based planning and control. V…
Training Auto-encoder-based Optimizers for Terahertz Image Reconstruction
Tak Ming Wong, Matthias Kahl, Peter Haring Bolívar +2
Terahertz (THz) sensing is a promising imaging technology for a wide variety of different applications. Extracting the interpretable and physically meaningful parameters for such a…
Controlling Neural Networks via Energy Dissipation
Michael Moeller, Thomas Möllenhoff, Daniel Cremers
The last decade has shown a tremendous success in solving various computer vision problems with the help of deep learning techniques. Lately, many works have demonstrated that lear…
Lifting Layers: Analysis and Applications
Peter Ochs, Tim Meinhardt, Laura Leal-Taixe +1
The great advances of learning-based approaches in image processing and computer vision are largely based on deeply nested networks that compose linear transfer functions with suit…
Composite Optimization by Nonconvex Majorization-Minimization
Jonas Geiping, Michael Moeller
The minimization of a nonconvex composite function can model a variety of imaging tasks. A popular class of algorithms for solving such problems are majorization-minimization techn…