research
∙
09/15/2023
Generalised Probabilistic Diffusion Scale-Spaces
Probabilistic diffusion models excel at sampling new images from learned...
share
research
∙
03/17/2023
Efficient Neural Generation of 4K Masks for Homogeneous Diffusion Inpainting
With well-selected data, homogeneous diffusion inpainting can reconstruc...
research
∙
03/14/2023
Generalised Scale-Space Properties for Probabilistic Diffusion Models
Probabilistic diffusion models enjoy increasing popularity in the deep l...
research
∙
02/11/2022
A Wasserstein GAN for Joint Learning of Inpainting and its Spatial Optimisation
Classic image inpainting is a restoration method that reconstructs missi...
research
∙
10/06/2021
Learning Sparse Masks for Diffusion-based Image Inpainting
Diffusion-based inpainting is a powerful tool for the reconstruction of ...
research
∙
08/31/2021
Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods
Partial differential equation (PDE) models and their associated variatio...
research
∙
07/30/2021
Connections between Numerical Algorithms for PDEs and Neural Networks
We investigate numerous structural connections between numerical algorit...
research
∙
03/29/2021
Translating Numerical Concepts for PDEs into Neural Architectures
We investigate what can be learned from translating numerical algorithms...
research
∙
02/07/2020
Translating Diffusion, Wavelets, and Regularisation into Residual Networks
Convolutional neural networks (CNNs) often perform well, but their stabi...
research
∙
06/20/2017