no code implementations • 18 Sep 2023 • Tim J. Adler, Jan-Hinrich Nölke, Annika Reinke, Minu Dietlinde Tizabi, Sebastian Gruber, Dasha Trofimova, Lynton Ardizzone, Paul F. Jaeger, Florian Buettner, Ullrich Köthe, Lena Maier-Hein
Current deep learning-based solutions for image analysis tasks are commonly incapable of handling problems to which multiple different plausible solutions exist.
1 code implementation • 20 Mar 2023 • The-Gia Leo Nguyen, Lynton Ardizzone, Ullrich Köthe
Autoencoders are able to learn useful data representations in an unsupervised matter and have been widely used in various machine learning and computer vision tasks.
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.
no code implementations • 21 Mar 2022 • Jana Fragemann, Lynton Ardizzone, Jan Egger, Jens Kleesiek
Encouraging the latent representation of a generative model to be disentangled offers new perspectives of control and interpretability.
no code implementations • 31 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.
1 code implementation • 5 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.
no code implementations • 26 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.
no code implementations • 15 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.
no code implementations • 10 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.
no code implementations • 14 Oct 2020 • Jens Müller, Robert Schmier, Lynton Ardizzone, Carsten Rother, Ullrich Köthe
Standard supervised learning breaks down under data distribution shift.
2 code implementations • CVPR 2021 • Radek Mackowiak, Lynton Ardizzone, Ullrich Köthe, Carsten Rother
Generative classifiers (GCs) are a promising class of models that are said to naturally accomplish these qualities.
2 code implementations • 13 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.
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.
no code implementations • 5 Nov 2019 • Tim J. Adler, Leonardo Ayala, Lynton Ardizzone, Hannes G. Kenngott, Anant Vemuri, Beat P. Müller-Stich, Carsten Rother, Ullrich Köthe, Lena Maier-Hein
Multispectral optical imaging is becoming a key tool in the operating room.
Out-of-Distribution Detection Out of Distribution (OOD) Detection
no code implementations • 25 Sep 2019 • Lynton Ardizzone, Carsten Lüth, Jakob Kruse, Carsten Rother, Ullrich Köthe
In this work, we address the task of natural image generation guided by a conditioning input.
6 code implementations • 4 Jul 2019 • Lynton Ardizzone, Carsten Lüth, Jakob Kruse, Carsten Rother, Ullrich Köthe
We demonstrate these properties for the tasks of MNIST digit generation and image colorization.
no code implementations • 8 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.
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.