# Resolution-Invariant Image Classification based on Fourier Neural Operators

Source: [Resolution-Invariant Image Classification based on Fourier Neural Operators](<https://www.sota2.com/research/paper/68fa5a3b1e209131ce9be001>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T16:22:39.889Z

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

Published: 2023-04-02T10:23:36.000Z

## 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 Fr\\'echet-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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2304.01227>)

[PDF](<https://arxiv.org/pdf/2304.01227.pdf>)