AI RESEARCH
Light-ResKAN: A Parameter-Sharing Lightweight KAN with Gram Polynomials for Efficient SAR Image Recognition
arXiv CS.CV
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ArXi:2604.01903v1 Announce Type: new Synthetic Aperture Radar (SAR) image recognition is vital for disaster monitoring, military reconnaissance, and ocean observation. However, large SAR image sizes hinder deep learning deployment on resource-constrained edge devices, and existing lightweight models struggle to balance high-precision feature extraction with low computational requirements. The emerging Kolmogoro-Arnold Network (KAN) enhances fitting by replacing fixed activations with learnable ones, reducing parameters and computation.