Periodic Activation Function

Periodic activation functions are increasingly used in neural networks to improve representation learning, particularly for periodic or frequency-rich data. Current research focuses on applying these functions within implicit neural representations (INRs) and other architectures like autoencoders, aiming to enhance signal processing, motion analysis, and anomaly detection. This approach offers advantages in sample efficiency, faster training, and improved accuracy across diverse applications, from image compression and control systems to modeling complex dynamical systems. The resulting improvements in representation learning and model performance have significant implications for various scientific fields and industrial applications.