44 citations · 84 across the 8 of their papers we have counts for
6 papers · 1 filter
Affine Self Convolution
Nichita Diaconu, Daniel E Worrall
Attention mechanisms, and most prominently self-attention, are a powerful building block for processing not only text but also images. These provide a parameter efficient method fo…
Chest CT Super-resolution and Domain-adaptation using Memory-efficient 3D Reversible GANs
Tycho F. A. van der Ouderaa, Daniel E. Worrall, Bram van Ginneken
Recently, paired (e.g. Pix2pix) and unpaired (e.g. CycleGAN) image-to-image translation methods have shown effective in medical imaging tasks. In practice, however, it can be diffi…
Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement
Ryutaro Tanno, Daniel Worrall, Enrico Kaden +6
Deep learning (DL) has shown great potential in medical image enhancement problems, such as super-resolution or image synthesis. However, to date, little consideration has been giv…
Deep Scale-spaces: Equivariance Over Scale
Daniel E. Worrall, Max Welling
We introduce deep scale-spaces (DSS), a generalization of convolutional neural networks, exploiting the scale symmetry structure of conventional image recognition tasks. Put plainl…
Learning to Convolve: A Generalized Weight-Tying Approach
Nichita Diaconu, Daniel E Worrall
Recent work (Cohen & Welling, 2016) has shown that generalizations of convolutions, based on group theory, provide powerful inductive biases for learning. In these generalizations,…
Reversible GANs for Memory-efficient Image-to-Image Translation
Tycho F. A. van der Ouderaa, Daniel E. Worrall
The Pix2pix and CycleGAN losses have vastly improved the qualitative and quantitative visual quality of results in image-to-image translation tasks. We extend this framework by exp…