Resolution-Invariant Image Classification Based on Fourier Neural Operators

Kabri S, Roith T, Tenbrinck D, Burger M (2023)

Publication Type: Conference contribution

Publication year: 2023

Journal

Lecture Notes in Computer Science Springer Verlag

Original Authors: Samira Kabri, Tim Roith, Daniel Tenbrinck, Martin Burger

Series: International Conference, SSVM

Book Volume: LNCS. volume 14009

Pages Range: 236-249

Conference Proceedings Title: Scale Space and Variational Methods in Computer Vision

Event location: Santa Margherita di Pula IT

ISBN: 9783031319747

DOI: 10.1007/978-3-031-31975-4_18

Abstract

In this paper we investigate the use of Fourier Neural Operators (FNOs) for image classification in comparison to standard Convolutional Neural Networks (CNNs). Neural operators are a discretization-invariant generalization of neural networks to approximate operators between infinite dimensional function spaces. FNOs—which are neural operators with a specific parametrization—have been applied successfully in the context of parametric PDEs. We derive the FNO architecture as an example for continuous and Fréchet-differentiable neural operators on Lebesgue spaces. We further show how CNNs can be converted into FNOs and vice versa and propose an interpolation-equivariant adaptation of the architecture.

Authors with CRIS profile

Samira Kabri Lehrstuhl für Angewandte Mathematik (Modellierung und Numerik) Tim Roith Lehrstuhl für Angewandte Mathematik (Modellierung und Numerik) Daniel Tenbrinck Lehrstuhl für Angewandte Mathematik (Modellierung und Numerik) Martin Burger

Involved external institutions

Deutsches Elektronen-Synchrotron DESY DE Germany (DE)

How to cite

APA:

Kabri, S., Roith, T., Tenbrinck, D., & Burger, M. (2023). Resolution-Invariant Image Classification Based on Fourier Neural Operators. In Scale Space and Variational Methods in Computer Vision (pp. 236-249). Santa Margherita di Pula, IT.

MLA:

Kabri, Samira, et al. "Resolution-Invariant Image Classification Based on Fourier Neural Operators." Proceedings of the International Conference on Scale Space and Variational Methods in Computer Vision, Santa Margherita di Pula 2023. 236-249.

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