Kolmogorov-Arnold Convolutions: Design Principles and Empirical Studies
<|reference_start|>Kolmogorov-Arnold Convolutions: Design Principles and Empirical Studies: The emergence of Kolmogorov-Arnold Networks (KANs) has sparked significant interest and debate within the scientific community. This paper explores the application of KANs in the domain of computer vision (CV). We examine the convolutional version of KANs, considering various nonlinearity options beyond splines, such as Wavelet transforms and a range of polynomials. We propose a parameter-efficient design for Kolmogorov-Arnold convolutional layers and a parameter-efficient finetuning algorithm for pre-trained KAN models, as well as KAN convolutional versions of self-attention and focal modulation layers. We provide empirical evaluations conducted on MNIST, CIFAR10, CIFAR100, Tiny ImageNet, ImageNet1k, and HAM10000 datasets for image classification tasks. Additionally, we explore segmentation tasks, proposing U-Net-like architectures with KAN convolutions, and achieving state-of-the-art results on BUSI, GlaS, and CVC datasets. We summarized all of our findings in a preliminary design guide of KAN convolutional models for computer vision tasks. Furthermore, we investigate regularization techniques for KANs. All experimental code and implementations of convolutional layers and models, pre-trained on ImageNet1k weights are available on GitHub via this https://github.com/IvanDrokin/torch-conv-kan<|reference_end|>
@article{drokin2024kolmogorov-arnold,
    title={Kolmogorov-Arnold Convolutions: Design Principles and Empirical Studies},
    author={Ivan Drokin},
    journal={arXiv preprint arXiv:2407.01092},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.01092},
    primaryClass={cs.CV cs.AI cs.LG}
}