SASNet: Spatially-Adaptive Sinusoidal Neural Networks

Abstract

Sinusoidal neural networks (SNNs) have emerged as powerful implicit neural representations (INRs) for low-dimensional signals in computer vision and graphics. They enable high-frequency signal reconstruction and smooth manifold modeling; however, they often suffer from spectral bias, training instability, and overfitting. To address these challenges, we propose SASNet, Spatially-Adaptive SNNs that robustly enhance the capacity of compact INRs to fit detailed signals. SASNet integrates a frequency embedding layer to control frequency components and mitigate spectral bias, along with jointly optimized, spatially-adaptive masks that localize neuron influence, reducing network redundancy and improving convergence stability. Robust to hyperparameter selection, SASNet faithfully reconstructs high-frequency signals without overfitting low-frequency regions. Our experiments show that SASNet outperforms state-of-the-art INRs, achieving strong fitting accuracy, super-resolution capability, and noise suppression, without sacrificing model compactness.