Adaptive Activation Function

Adaptive activation functions (AAFs) aim to improve neural network performance by dynamically adjusting their shape during training, unlike traditional fixed functions. Current research focuses on developing novel AAF architectures, such as those based on piecewise linear approximations, spline functions, and transformations of existing functions, often within lightweight networks or for specific applications like time series analysis and physics-informed neural networks. These advancements offer the potential for increased accuracy and efficiency in various machine learning tasks, particularly in resource-constrained environments and scenarios with limited data, leading to improved model performance and interpretability.