In this paper, we present Convolutional Kolmogorov-Arnold Networks, a novel architecture that integrates the learnable spline-based activation functions of Kolmogorov-Arnold Networks (KANs) into convolutional layers. By replacing traditional fixed-weight kernels with learnable non-linear functions, Convolutional KANs offer a significant improvement in parameter efficiency and expressive power over standard Convolutional Neural Networks (CNNs). We empirically evaluate Convolutional KANs on the Fashion-MNIST dataset, demonstrating competitive accuracy with up to 50% fewer parameters compared to baseline classic convolutions. This suggests that the KAN Convolution can effectively capture complex spatial relationships with fewer resources, offering a promising alternative for parameter-efficient deep learning models.
View on arXiv@article{bodner2025_2406.13155,
title={ Convolutional Kolmogorov-Arnold Networks },
author={ Alexander Dylan Bodner and Antonio Santiago Tepsich and Jack Natan Spolski and Santiago Pourteau },
journal={arXiv preprint arXiv:2406.13155},
year={ 2025 }
}