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

• URL: http://arxiv.org/abs/2409.10836v2
• Date: Wed, 18 Sep 2024 18:34:56 GMT
• Title: Single-Layer Learnable Activation for Implicit Neural Representation (SL$^{2}$A-INR)
• Authors: Moein Heidari, Reza Rezaeian, Reza Azad, Dorit Merhof, Hamid Soltanian-Zadeh, Ilker Hacihaliloglu,
• Abstract summary: Implicit Representation (INR) leveraging a neural network to transform coordinate input into corresponding attributes has driven significant advances in vision-related domains. We propose SL$2$A-INR with a single-layer learnable activation function, prompting the effectiveness of traditional ReLU-baseds. Our method performs superior across diverse tasks, including image representation, 3D shape reconstruction, single image super-resolution, CT reconstruction, and novel view.
• Score: 6.572456394600755
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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