264 citations · 320 across the 7 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…
Towards Multimodal Depth Estimation from Light Fields
Titus Leistner, Radek Mackowiak, Lynton Ardizzone +2
Light field applications, especially light field rendering and depth estimation, developed rapidly in recent years. While state-of-the-art light field rendering methods handle semi…
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…
Representing Ambiguity in Registration Problems with Conditional Invertible Neural Networks
Darya Trofimova, Tim Adler, Lisa Kausch +5
Image registration is the basis for many applications in the fields of medical image computing and computer assisted interventions. One example is the registration of 2D X-ray imag…
Generative Classifiers as a Basis for Trustworthy Image Classification
Radek Mackowiak, Lynton Ardizzone, Ullrich Köthe +1
With the maturing of deep learning systems, trustworthiness is becoming increasingly important for model assessment. We understand trustworthiness as the combination of explainabil…
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…