Search Results for author: Jakob Kruse

Found 8 papers, 4 papers with code

Conditional Invertible Neural Networks for Diverse Image-to-Image Translation

1 code implementation5 May 2021 Lynton Ardizzone, Jakob Kruse, Carsten Lüth, Niels Bracher, Carsten Rother, Ullrich Köthe

We introduce a new architecture called a conditional invertible neural network (cINN), and use it to address the task of diverse image-to-image translation for natural images.

Colorization Image-to-Image Translation +1

Benchmarking Invertible Architectures on Inverse Problems

no code implementations26 Jan 2021 Jakob Kruse, Lynton Ardizzone, Carsten Rother, Ullrich Köthe

Recent work demonstrated that flow-based invertible neural networks are promising tools for solving ambiguous inverse problems.

Technical report: Training Mixture Density Networks with full covariance matrices

no code implementations4 Mar 2020 Jakob Kruse

Mixture Density Networks are a tried and tested tool for modelling conditional probability distributions.

HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference

1 code implementation25 May 2019 Jakob Kruse, Gianluca Detommaso, Ullrich Köthe, Robert Scheichl

Many recent invertible neural architectures are based on coupling block designs where variables are divided in two subsets which serve as inputs of an easily invertible (usually affine) triangular transformation.

Bayesian Inference Density Estimation

Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks

no code implementations8 Mar 2019 Tim J. Adler, Lynton Ardizzone, Anant Vemuri, Leonardo Ayala, Janek Gröhl, Thomas Kirchner, Sebastian Wirkert, Jakob Kruse, Carsten Rother, Ullrich Köthe, Lena Maier-Hein

Assessment of the specific hardware used in conjunction with such algorithms, however, has not properly addressed the possibility that the problem may be ill-posed.

Analyzing Inverse Problems with Invertible Neural Networks

2 code implementations ICLR 2019 Lynton Ardizzone, Jakob Kruse, Sebastian Wirkert, Daniel Rahner, Eric W. Pellegrini, Ralf S. Klessen, Lena Maier-Hein, Carsten Rother, Ullrich Köthe

Often, the forward process from parameter- to measurement-space is a well-defined function, whereas the inverse problem is ambiguous: one measurement may map to multiple different sets of parameters.

Cannot find the paper you are looking for? You can Submit a new open access paper.