Exploring the Relationship: Transformative Adaptive Activation Functions in Comparison to Other Activation Functions
This work provides a comprehensive comparison for researchers in machine learning, but it is incremental as it positions TAAFs within existing literature without introducing new methods or data.
The paper tackled the problem of understanding how transformative adaptive activation functions (TAAFs) compare to other activation functions in neural networks, showing that TAAFs generalize over 50 existing functions and utilize concepts from over 70 others.
Neural networks are the state-of-the-art approach for many tasks and the activation function is one of the main building blocks that allow such performance. Recently, a novel transformative adaptive activation function (TAAF) allowing for any vertical and horizontal translation and scaling was proposed. This work sets the TAAF into the context of other activation functions. It shows that the TAAFs generalize over 50 existing activation functions and utilize similar concepts as over 70 other activation functions, underscoring the versatility of TAAFs. This comprehensive exploration positions TAAFs as a promising and adaptable addition to neural networks.