Residual Kolmogorov-Arnold Network for Enhanced Deep Learning

• URL: http://arxiv.org/abs/2410.05500v1
• Date: Mon, 7 Oct 2024 21:12:32 GMT
• Title: Residual Kolmogorov-Arnold Network for Enhanced Deep Learning
• Authors: Ray Congrui Yu, Sherry Wu, Jiang Gui,
• Abstract summary: We introduce Residual Arnold, which incorporates the Kolmogorov-KAN framework as a residual component. Our results demonstrate the potential of RKAN to enhance the capabilities of deep CNNs in visual data.
• Score: 0.5852077003870417
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the strong performance in many computer vision tasks, Convolutional Neural Networks (CNNs) can sometimes struggle to efficiently capture long-range, complex non-linear dependencies in deeper layers of the network. We address this limitation by introducing Residual KAN, which incorporates the Kolmogorov-Arnold Network (KAN) within the CNN framework as a residual component. Our approach uses Chebyshev polynomials as the basis for KAN convolutions that enables more expressive and adaptive feature representations while maintaining computational efficiency. The proposed RKAN blocks, when integrated into established architectures such as ResNet and DenseNet, offer consistent improvements over the baseline models on various well-known benchmarks. Our results demonstrate the potential of RKAN to enhance the capabilities of deep CNNs in visual data.

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