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08/28/2023
Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Imaging Inverse Problems
Denoising diffusion models have emerged as the go-to framework for solvi...
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08/27/2023
Score-Based Generative Models for PET Image Reconstruction
Score-based generative models have demonstrated highly promising results...
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03/28/2023
SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction
The deep image prior (DIP) is a well-established unsupervised deep learn...
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02/20/2023
Fast and Painless Image Reconstruction in Deep Image Prior Subspaces
The deep image prior (DIP) is a state-of-the-art unsupervised approach f...
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10/10/2022
Sampling-based inference for large linear models, with application to linearised Laplace
Large-scale linear models are ubiquitous throughout machine learning, wi...
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07/11/2022
Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior
We investigate adaptive design based on a single sparse pilot scan for g...
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06/17/2022
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning
The linearised Laplace method for estimating model uncertainty has recei...
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02/28/2022
A Probabilistic Deep Image Prior for Computational Tomography
Existing deep-learning based tomographic image reconstruction methods do...
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10/22/2021
Conditional Variational Autoencoder for Learned Image Reconstruction
Learned image reconstruction techniques using deep neural networks have ...
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07/06/2021
Unsupervised Knowledge-Transfer for Learned Image Reconstruction
Deep learning-based image reconstruction approaches have demonstrated im...
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11/17/2020
Quantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction
Image reconstruction methods based on deep neural networks have shown ou...
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07/20/2020
