
Representing Ambiguity in Registration Problems with Conditional Invertible Neural Networks
Image registration is the basis for many applications in the fields of m...
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Invertible Neural Networks for Uncertainty Quantification in Photoacoustic Imaging
Multispectral photoacoustic imaging (PAI) is an emerging imaging modalit...
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Learning Robust Models Using The Principle of Independent Causal Mechanisms
Standard supervised learning breaks down under data distribution shift. ...
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Generative Classifiers as a Basis for Trustworthy Computer Vision
With the maturing of deep learning systems, trustworthiness is becoming ...
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BayesFlow: Learning complex stochastic models with invertible neural networks
Estimating the parameters of mathematical models is a common problem in ...
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Exact Information Bottleneck with Invertible Neural Networks: Getting the Best of Discriminative and Generative Modeling
The Information Bottleneck (IB) principle offers a unified approach to m...
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Out of distribution detection for intraoperative functional imaging
Multispectral optical imaging is becoming a key tool in the operating ro...
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Guided Image Generation with Conditional Invertible Neural Networks
In this work, we address the task of natural image generation guided by ...
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HINT: Hierarchical Invertible Neural Transport for General and Sequential Bayesian inference
In this paper, we introduce Hierarchical Invertible Neural Transport (HI...
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Uncertaintyaware performance assessment of optical imaging modalities with invertible neural networks
Purpose: Optical imaging is evolving as a key technique for advanced sen...
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Analyzing Inverse Problems with Invertible Neural Networks
In many tasks, in particular in natural science, the goal is to determin...
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Lynton Ardizzone
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