Exploring the Relationship: Transformative Adaptive Activation Functions in Comparison to Other Activation Functions

TL;DR

This paper investigates Transformative Adaptive Activation Functions (TAAFs), demonstrating their ability to generalize over 50 existing functions and incorporate concepts from over 70 others, highlighting their versatility and potential in neural networks.

Contribution

The work contextualizes TAAFs among existing activation functions, showing they unify and extend many previous approaches, thus offering a highly adaptable activation mechanism.

Findings

TAAFs generalize over 50 existing activation functions.

TAAFs incorporate concepts from over 70 other activation functions.

TAAFs are positioned as a versatile addition to neural network design.

Abstract

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.