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09/18/2023
Application-driven Validation of Posteriors in Inverse Problems
Current deep learning-based solutions for image analysis tasks are commo...
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03/20/2023
Training Invertible Neural Networks as Autoencoders
Autoencoders are able to learn useful data representations in an unsuper...
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03/30/2022
Towards Multimodal Depth Estimation from Light Fields
Light field applications, especially light field rendering and depth est...
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03/21/2022
Review of Disentanglement Approaches for Medical Applications – Towards Solving the Gordian Knot of Generative Models in Healthcare
Deep neural networks are commonly used for medical purposes such as imag...
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01/31/2022
Exoplanet Characterization using Conditional Invertible Neural Networks
The characterization of an exoplanet's interior is an inverse problem, w...
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05/05/2021
Conditional Invertible Neural Networks for Diverse Image-to-Image Translation
We introduce a new architecture called a conditional invertible neural n...
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01/26/2021
Benchmarking Invertible Architectures on Inverse Problems
Recent work demonstrated that flow-based invertible neural networks are ...
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12/15/2020
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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11/10/2020
Invertible Neural Networks for Uncertainty Quantification in Photoacoustic Imaging
Multispectral photoacoustic imaging (PAI) is an emerging imaging modalit...
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10/14/2020
Learning Robust Models Using The Principle of Independent Causal Mechanisms
Standard supervised learning breaks down under data distribution shift. ...
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07/29/2020
Generative Classifiers as a Basis for Trustworthy Computer Vision
With the maturing of deep learning systems, trustworthiness is becoming ...
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03/13/2020
BayesFlow: Learning complex stochastic models with invertible neural networks
Estimating the parameters of mathematical models is a common problem in ...
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01/17/2020
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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11/05/2019
Out of distribution detection for intra-operative functional imaging
Multispectral optical imaging is becoming a key tool in the operating ro...
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07/04/2019
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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05/25/2019
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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03/08/2019
Uncertainty-aware 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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08/14/2018
