54 citations · 226 across the 24 of their papers we have counts for
27 papers
A Bifurcation-Based Domain Decomposition Method with Neural Operators for Blood Flow Simulation
Yuzhou Zhao, Han Zhang, J. Matias Di Martino +2
Fast and accurate simulation of hemodynamic behavior within vascular networks is essential for numerous clinical applications. However, obtaining high-quality and computationally e…
Fast, nonlocal and neural: a lightweight high quality solution to image denoising
Yu Guo, Axel Davy, Gabriele Facciolo +2
With the widespread application of convolutional neural networks (CNNs), the traditional model based denoising algorithms are now outperformed. However, CNNs face two problems. Fir…
Collaborative Blind Image Deblurring
Thomas Eboli, Jean-Michel Morel, Gabriele Facciolo
Blurry images usually exhibit similar blur at various locations across the image domain, a property barely captured in nowadays blind deblurring neural networks. We show that when…
SING: A Plug-and-Play DNN Learning Technique
Adrien Courtois, Damien Scieur, Jean-Michel Morel +2
We propose SING (StabIlized and Normalized Gradient), a plug-and-play technique that improves the stability and generalization of the Adam(W) optimizer. SING is straightforward to…
Scaling Painting Style Transfer
Bruno Galerne, Lara Raad, José Lezama +1
Neural style transfer (NST) is a deep learning technique that produces an unprecedentedly rich style transfer from a style image to a content image. It is particularly impressive w…
Can neural networks extrapolate? Discussion of a theorem by Pedro Domingos
Adrien Courtois, Jean-Michel Morel, Pablo Arias
Neural networks trained on large datasets by minimizing a loss have become the state-of-the-art approach for resolving data science problems, particularly in computer vision, image…