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Convolutional Kolmogorov-Arnold Networks

32 Citations•2024•
Alexander Dylan Bodner, Antonio Santiago Tepsich, Jack Natan Spolski
ArXiv

Experiments show that KAN Convolutions seem to learn more per kernel, which opens up a new horizon of possibilities in deep learning for computer vision.

Abstract

In this paper, we introduce Convolutional Kolmogorov-Arnold Networks (Convolutional KANs), an innovative alternative to the standard Convolutional Neural Networks (CNNs) that have revolutionized the field of computer vision. By integrating the learneable non-linear activation functions presented in Kolmogorov-Arnold Networks (KANs) into convolutions, we propose a new layer. Throughout the paper, we empirically validate the performance of Convolutional KANs against traditional architectures across Fashion-MNIST dataset, finding that, in some cases, this new approach maintains a similar level of accuracy while using half the number of parameters. This experiments show that KAN Convolutions seem to learn more per kernel, which opens up a new horizon of possibilities in deep learning for computer vision.