264 citations · 937 across the 33 of their papers we have counts for
6 papers · 1 filter
Application-driven Validation of Posteriors in Inverse Problems
Tim J. Adler, Jan-Hinrich Nölke, Annika Reinke +8
Current deep learning-based solutions for image analysis tasks are commonly incapable of handling problems to which multiple different plausible solutions exist. In response, poste…
Conditional Invertible Neural Networks for Diverse Image-to-Image Translation
Lynton Ardizzone, Jakob Kruse, Carsten Lüth +3
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. This…
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…
The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning
Steffen Wolf, Alberto Bailoni, Constantin Pape +4
Image partitioning, or segmentation without semantics, is the task of decomposing an image into distinct segments, or equivalently to detect closed contours. Most prior work either…
Learned Watershed: End-to-End Learning of Seeded Segmentation
Steffen Wolf, Lukas Schott, Ullrich Köthe +1
Learned boundary maps are known to outperform hand- crafted ones as a basis for the watershed algorithm. We show, for the first time, how to train watershed computation jointly wit…