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English(EN) FUTON: Fourier Tensor Networks for Implicit Neural Representations

FUTON:新的傅里叶张量网络增强了隐式神经表示

研究人员推出了一种用于隐式神经表示(INRs)的新方法——FUTON(傅里叶张量网络)。FUTON 将信号建模为广义傅里叶级数,利用低秩张量分解来参数化系数。该方法结合了傅里叶基数在平滑性和周期性方面的归纳偏置,以及低秩参数化以强制执行低维谱结构。在图像拟合、占用体积和新视图合成方面的实验表明,FUTON 在匹配大小的情况下,其平均质量优于其他十二种 INRs,收敛速度显著更快,并且在训练期间需要更少的内存。 AI

影响 引入了一种新的隐式神经表示方法,该方法显示出更快的收敛速度和更低的内存使用量,有望提高信号编码任务的效率。

排序理由 该集群描述了一篇介绍隐式神经表示新方法的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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FUTON:新的傅里叶张量网络增强了隐式神经表示

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该集群描述了一篇介绍隐式神经表示新方法的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Pooya Ashtari, Pourya Behmandpoor, Nikos Deligiannis, Aleksandra Pizurica ·

    FUTON:用于隐式神经表示的傅里叶张量网络

    arXiv:2602.13414v2 Announce Type: replace-cross Abstract: Implicit neural representations (INRs) have emerged as powerful tools for encoding signals, yet dominant MLP-based designs often suffer from slow convergence, and how their accuracy depends on their size is known only empi…