1 citations · 1 across the 5 of their papers we have counts for
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UNet-AF: Alias-free UNet architectures
Jérémy Scanvic, Quentin Barthélemy, Julián Tachella
The simplicity and effectiveness of UNet architectures make them ubiquitous in image restoration, segmentation, and diffusion models. They are often assumed to be equivariant to tr…
Equivariant Splitting: Self-supervised learning from incomplete data
Victor Sechaud, Jérémy Scanvic, Quentin Barthélemy +2
Self-supervised learning for inverse problems allows to train a reconstruction network from noise and/or incomplete data alone. These methods have the potential of enabling learnin…
Translation-Equivariance of Normalization Layers and Aliasing in Convolutional Neural Networks
Jérémy Scanvic, Quentin Barthélemy, Julián Tachella
The design of convolutional neural architectures that are exactly equivariant to continuous translations is an active field of research. It promises to benefit scientific computing…