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.

这篇论文提出了卷积科尔莫戈洛夫 - 阿诺德网络（Convolutional KANs），这是标准卷积神经网络（CNN）的一种创新替代方法，我们将科尔莫戈洛夫 - 阿诺德网络（KANs）中的非线性激活函数集成到卷积中，构建出一个新的层。我们在 MNIST 和 Fashion-MNIST 基准测试中对 Convolutional KANs 的性能进行了实证验证，表明这种新方法在准确性方面保持了类似水平，同时只使用了一半的参数。这种参数量的显著减少打开了推进神经网络架构优化的新途径。