English(EN) Spatial-Frequency-Aware Implicit Neural Representation of Multidimensional Signals via MLP-KAN Fusion

新的INR框架融合MLP和KAN以改进信号表示

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员开发了一个新的隐式神经表示（INR）框架，该框架融合了多层感知器（MLP）和Kolmogorov-Arnold网络（KAN），以更好地建模多维信号。这种混合方法解决了传统MLP的光谱偏差问题（倾向于低频），因为它允许KAN分支捕获高频细节。该框架集成了离散小波变换，以分解和融合两个分支的输出，鼓励互补的面向频率的建模。在图像、体积和光场等各种信号类型上的实验表明，重建保真度有所提高。 AI

影响这项研究可能带来更准确、更详细的复杂数据建模，从而惠及那些依赖高保真信号重建的领域。

排序理由该集群包含一篇详细介绍新颖信号表示技术方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Wen Yan, Ligen Shi, Jun Qiu, Haimiao Zhang, Lina Wu, Chang Liu · 2026-10-09 04:00

MLP-KAN 融合的多维信号空间-频率感知隐式神经表示

arXiv:2610.11296v1 Announce Type: new Abstract: Implicit Neural Representations (INRs) have emerged as a compelling paradigm for modeling multidimensional signals by mapping continuous coordinates to signal values. However, Multi-Layer Perceptrons (MLP)-based INRs inherently suff…

本文如何被排名

报道来源 [1]

MLP-KAN 融合的多维信号空间-频率感知隐式神经表示

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