Convolutional Kolmogorov-Arnold Networks
<|reference_start|>Convolutional Kolmogorov-Arnold Networks: 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.<|reference_end|>
@article{bodner2024convolutional,
    title={Convolutional Kolmogorov-Arnold Networks},
    author={Alexander Dylan Bodner, Antonio Santiago Tepsich, Jack Natan Spolski,
  Santiago Pourteau},
    journal={arXiv preprint arXiv:2406.13155},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.13155},
    primaryClass={cs.CV cs.AI}
}