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cs.LG2026
Limits of Resolution Equivariance in Fourier Neural Operators
Alex Colagrande, Paul Caillon, Eva Feillet +1
Fourier Neural Operators are often assumed to generalize across spatial resolutions, enabling training on a coarse grid and deployment on a finer grid. We test this assumption by c…
cs.LG2024
Recommendation of data-free class-incremental learning algorithms by simulating future data
Eva Feillet, Adrian Popescu, Céline Hudelot
Class-incremental learning deals with sequential data streams composed of batches of classes. Various algorithms have been proposed to address the challenging case where samples fr…
cs.LG2023
An Analysis of Initial Training Strategies for Exemplar-Free Class-Incremental Learning
Grégoire Petit, Michael Soumm, Eva Feillet +4
Class-Incremental Learning (CIL) aims to build classification models from data streams. At each step of the CIL process, new classes must be integrated into the model. Due to catas…