KAConvNet: Kolmogorov-Arnold Convolutional Networks for Vision Recognition
The Convolutional Neural Networks (CNNs) have been the dominant and effective approach for general computer vision tasks. Recently, Kolmogorov-Arnold neural networks (KANs), based on the Kolmogorov-Arnold representation theorem, have shown potential to replace Multi-Layer Perceptrons (MLPs) in deep learning. KANs, which use learnable nonlinear activations on edges and simple summation on nodes, offer fewer parameters and greater explainability compared to MLPs. However, there has been limited exploration of integrating the Kolmogorov-Arnold representation theorem with convolutional methods for computer vision tasks. Existing attempts have merely replaced learnable activation functions wit...
| Authors | Zhaoxiang Liu, Zhicheng Ma, Kaikai Zhao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 25 April 2026 |
| Last updated at source | 25 April 2026 |
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