單層可學習激活函數用於隱式神經表示（SL^{2}A-INR）

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

September 17, 2024

作者: Moein Heidari, Reza Rezaeian, Reza Azad, Dorit Merhof, Hamid Soltanian-Zadeh, Ilker Hacihaliloglu

cs.AI

摘要

隱式神經表示（INR）利用神經網絡將座標輸入轉換為相應屬性，最近在幾個與視覺相關的領域中推動了顯著進展。然而，INR的性能受其多層感知器（MLP）結構中所使用的非線性激活函數的選擇影響甚巨。已經研究了多種非線性，但目前的INR在捕捉高頻成分、多樣信號類型和處理反問題方面存在限制。我們確定這些問題可以通過在INR中引入範式轉變來大大緩解。我們發現，在初始層中具有可學習激活的架構可以表示底層信號中的細節。具體而言，我們提出了SL^{2}A-INR，這是一個具有單層可學習激活函數的INR混合網絡，促進了傳統基於ReLU的MLP的有效性。我們的方法在包括圖像表示、3D形狀重建、修補、單張圖像超分辨率、CT重建和新視角合成在內的多個任務上表現優越。通過全面實驗，SL^{2}A-INR為INR的準確性、質量和收斂速度設立了新的基準。

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.