ConvKAN: Towards Robust, High-Performance and Interpretable Image Classification

Achref Ouni, Chafik Samir, Yousef Bouaziz, Anis Fradi

2025

Abstract

This paper introduces ConvKAN, a novel convolutional model for image classification in artificial vision systems. ConvKAN integrates Kolmogorov-Arnold Networks (KANs) with convolutional layers within Con-volutional Neural Networks (CNNs). We demonstrate that this combination outperforms standard CNN-MLP architectures and state-of-the-art methods. Our study investigates the impact of this integration on classification performance across benchmarks and assesses the robustness of ConvKAN models compared to established CNN architectures. Varied and extensive experimental results show that ConvKAN achieves substantial gains in accuracy, precision, and recall, surpassing current state-of-the-art methods.

Paper Citation

in Harvard Style

Ouni A., Samir C., Bouaziz Y. and Fradi A. (2025). ConvKAN: Towards Robust, High-Performance and Interpretable Image Classification. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 48-58. DOI: 10.5220/0013120800003912

in Bibtex Style

@conference{visapp25,
author={Achref Ouni and Chafik Samir and Yousef Bouaziz and Anis Fradi},
title={ConvKAN: Towards Robust, High-Performance and Interpretable Image Classification},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2025},
pages={48-58},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013120800003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - ConvKAN: Towards Robust, High-Performance and Interpretable Image Classification
SN - 978-989-758-728-3
AU - Ouni A.
AU - Samir C.
AU - Bouaziz Y.
AU - Fradi A.
PY - 2025
SP - 48
EP - 58
DO - 10.5220/0013120800003912
PB - SciTePress