264 citations · 371 across the 13 of their papers we have counts for
7 papers · 1 filter
Benchmarking Invertible Architectures on Inverse Problems
Jakob Kruse, Lynton Ardizzone, Carsten Rother +1
Recent work demonstrated that flow-based invertible neural networks are promising tools for solving ambiguous inverse problems. Following up on this, we investigate how ten inverti…
Learning Robust Models Using The Principle of Independent Causal Mechanisms
Jens Müller, Robert Schmier, Lynton Ardizzone +2
Standard supervised learning breaks down under data distribution shift. However, the principle of independent causal mechanisms (ICM, Peters et al. (2017)) can turn this weakness i…
MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models
Siddharth Tourani, Alexander Shekhovtsov, Carsten Rother +1
Dense, discrete Graphical Models with pairwise potentials are a powerful class of models which are employed in state-of-the-art computer vision and bio-imaging applications. This w…
Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization
Siddharth Tourani, Alexander Shekhovtsov, Carsten Rother +1
We consider the maximum-a-posteriori inference problem in discrete graphical models and study solvers based on the dual block-coordinate ascent rule. We map all existing solvers in…
Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)
Peter Sorrenson, Carsten Rother, Ullrich Köthe
A central question of representation learning asks under which conditions it is possible to reconstruct the true latent variables of an arbitrarily complex generative process. Rece…
Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification
Lynton Ardizzone, Radek Mackowiak, Carsten Rother +1
The Information Bottleneck (IB) objective uses information theory to formulate a task-performance versus robustness trade-off. It has been successfully applied in the standard disc…