Riccardo Barbano | DeepAI

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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...

0 Riccardo Barbano, et al. ∙

∙ 08/27/2023

Score-Based Generative Models for PET Image Reconstruction

Score-based generative models have demonstrated highly promising results...

0 Imraj RD Singh, et al. ∙

∙ 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...

0 Marco Nittscher, et al. ∙

∙ 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...

0 Riccardo Barbano, et al. ∙

∙ 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...

0 Javier Antoran, et al. ∙

∙ 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...

0 Riccardo Barbano, et al. ∙

∙ 06/17/2022

Adapting the Linearised Laplace Model Evidence for Modern Deep Learning

The linearised Laplace method for estimating model uncertainty has recei...

0 Javier Antoran, et al. ∙

∙ 02/28/2022

A Probabilistic Deep Image Prior for Computational Tomography

Existing deep-learning based tomographic image reconstruction methods do...

14 Javier Antoran, et al. ∙

∙ 10/22/2021

Conditional Variational Autoencoder for Learned Image Reconstruction

Learned image reconstruction techniques using deep neural networks have ...

0 Chen Zhang, et al. ∙

∙ 07/06/2021

Unsupervised Knowledge-Transfer for Learned Image Reconstruction

Deep learning-based image reconstruction approaches have demonstrated im...

37 Riccardo Barbano, et al. ∙

∙ 11/17/2020

Quantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction

Image reconstruction methods based on deep neural networks have shown ou...

16 Riccardo Barbano, et al. ∙

∙ 07/20/2020

Quantifying Model Uncertainty in Inverse Problems via Bayesian Deep Gradient Descent

Recent advances in reconstruction methods for inverse problems leverage ...

12 Riccardo Barbano, et al. ∙

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