  In this paper, we introduce the Convolutional Kolmogorov-Arnold Networks
(Convolutional KANs), an innovative alternative to the standard Convolutional
Neural Networks (CNNs) that have revolutionized the field of computer vision.
We integrate the non-linear activation functions presented in Kolmogorov-Arnold
Networks (KANs) into convolutions to build a new layer. Throughout the paper,
we empirically validate the performance of Convolutional KANs against
traditional architectures across MNIST and Fashion-MNIST benchmarks,
illustrating that this new approach maintains a similar level of accuracy while
using half the amount of parameters. This significant reduction of parameters
opens up a new approach to advance the optimization of neural network
architectures.
