单层可学习激活隐式神经表示（SL^{2}A-INR）

摘要

隐式神经表示（INR）利用神经网络将坐标输入转换为相应属性，最近在几个与视觉相关的领域取得了重大进展。然而，INR的性能受其多层感知器（MLP）架构中所使用的非线性激活函数选择的影响很大。已经研究了多种非线性激活函数；然而，当前的INR在捕获高频成分、多样信号类型和处理逆问题方面存在局限性。我们发现这些问题可以通过在INR中引入一种范式转变来极大缓解。我们发现，在初始层具有可学习激活的架构可以表示底层信号中的细节。具体而言，我们提出了SL^{2}A-INR，这是一个用于INR的混合网络，具有单层可学习激活函数，促进了传统基于ReLU的MLP的有效性。我们的方法在包括图像表示、3D形状重建、修补、单图像超分辨率、CT重建和新视角合成在内的多样任务中表现出色。通过全面实验，SL^{2}A-INR为INR设定了新的准确性、质量和收敛速度基准。

English

Implicit Neural Representation (INR), leveraging a neural network to transform coordinate input into corresponding attributes, has recently driven significant advances in several vision-related domains. However, the performance of INR is heavily influenced by the choice of the nonlinear activation function used in its multilayer perceptron (MLP) architecture. Multiple nonlinearities have been investigated; yet, current INRs face limitations in capturing high-frequency components, diverse signal types, and handling inverse problems. We have identified that these problems can be greatly alleviated by introducing a paradigm shift in INRs. We find that an architecture with learnable activations in initial layers can represent fine details in the underlying signals. Specifically, we propose SL^{2}A-INR, a hybrid network for INR with a single-layer learnable activation function, prompting the effectiveness of traditional ReLU-based MLPs. Our method performs superior across diverse tasks, including image representation, 3D shape reconstructions, inpainting, single image super-resolution, CT reconstruction, and novel view synthesis. Through comprehensive experiments, SL^{2}A-INR sets new benchmarks in accuracy, quality, and convergence rates for INR.

单层可学习激活隐式神经表示（SL^{2}A-INR）

Single-Layer Learnable Activation for Implicit Neural Representation (SL^{2}A-INR)

摘要

Summary

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