Search Results for author: Lynton Ardizzone

Found 16 papers, 6 papers with code

Towards Multimodal Depth Estimation from Light Fields

no code implementations CVPR 2022 Titus Leistner, Radek Mackowiak, Lynton Ardizzone, Ullrich Köthe, Carsten Rother

We argue that this is due current methods only considering a single "true" depth, even when multiple objects at different depths contributed to the color of a single pixel.

Depth Estimation Depth Prediction

Exoplanet Characterization using Conditional Invertible Neural Networks

no code implementations31 Jan 2022 Jonas Haldemann, Victor Ksoll, Daniel Walter, Yann Alibert, Ralf S. Klessen, Willy Benz, Ullrich Koethe, Lynton Ardizzone, Carsten Rother

Indeed, using cINNs allows for orders of magnitude faster inference of an exoplanet's composition than what is possible using an MCMC method, however, it still requires the computation of a large database of internal structures to train the cINN.

Bayesian Inference

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 Colorization +2

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.

Representing Ambiguity in Registration Problems with Conditional Invertible Neural Networks

no code implementations15 Dec 2020 Darya Trofimova, Tim Adler, Lisa Kausch, Lynton Ardizzone, Klaus Maier-Hein, Ulrich Köthe, Carsten Rother, Lena Maier-Hein

One example is the registration of 2D X-ray images with preoperative three-dimensional computed tomography (CT) images in intraoperative surgical guidance systems.

Computed Tomography (CT) Image Registration

Invertible Neural Networks for Uncertainty Quantification in Photoacoustic Imaging

no code implementations10 Nov 2020 Jan-Hinrich Nölke, Tim Adler, Janek Gröhl, Thomas Kirchner, Lynton Ardizzone, Carsten Rother, Ullrich Köthe, Lena Maier-Hein

Multispectral photoacoustic imaging (PAI) is an emerging imaging modality which enables the recovery of functional tissue parameters such as blood oxygenation.

BayesFlow: Learning complex stochastic models with invertible neural networks

1 code implementation13 Mar 2020 Stefan T. Radev, Ulf K. Mertens, Andreass Voss, Lynton Ardizzone, Ullrich Köthe

In addition, our method incorporates a summary network trained to embed the observed data into maximally informative summary statistics.

Bayesian Inference Epidemiology

Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification

3 code implementations NeurIPS 2020 Lynton Ardizzone, Radek Mackowiak, Carsten Rother, Ullrich Köthe

In this work, firstly, we develop the theory and methodology of IB-INNs, a class of conditional normalizing flows where INNs are trained using the IB objective: Introducing a small amount of {\em controlled} information loss allows for an asymptotically exact formulation of the IB, while keeping the INN's generative capabilities intact.

General Classification Out-of-Distribution Detection

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.

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