264 citations · 350 across the 5 of their papers we have counts for
6 papers
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…
Out of distribution detection for intra-operative functional imaging
Tim J. Adler, Leonardo Ayala, Lynton Ardizzone +6
Multispectral optical imaging is becoming a key tool in the operating room. Recent research has shown that machine learning algorithms can be used to convert pixel-wise reflectance…
Object Segmentation using Pixel-wise Adversarial Loss
Ricard Durall, Franz-Josef Pfreundt, Ullrich Köthe +1
Recent deep learning based approaches have shown remarkable success on object segmentation tasks. However, there is still room for further improvement. Inspired by generative adver…
Guided Image Generation with Conditional Invertible Neural Networks
Lynton Ardizzone, Carsten Lüth, Jakob Kruse +2
In this work, we address the task of natural image generation guided by a conditioning input. We introduce a new architecture called conditional invertible neural network (cINN). T…
Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks
Tim J. Adler, Lynton Ardizzone, Anant Vemuri +8
Purpose: Optical imaging is evolving as a key technique for advanced sensing in the operating room. Recent research has shown that machine learning algorithms can be used to addres…
How to Extract the Geometry and Topology from Very Large 3D Segmentations
Bjoern Andres, Ullrich Koethe, Thorben Kroeger +1
Segmentation is often an essential intermediate step in image analysis. A volume segmentation characterizes the underlying volume image in terms of geometric information--segments,…