Sinusoidal Activation

Sinusoidal activation functions are increasingly used in neural networks, particularly implicit neural representations (INRs), aiming to improve training speed, representation capacity, and the handling of high-dimensional data. Research focuses on optimizing initialization schemes, exploring alternative periodic and non-periodic activation functions (e.g., hyperbolic sinusoidal variations), and understanding the underlying mathematical properties of these networks, often through Fourier analysis and kernel methods. These advancements are impacting diverse fields, including computer vision, physics simulations, natural language processing, and medical image processing, by enabling more efficient and accurate modeling of complex signals and data.