Learn-Able Parameter Guided Activation Functions

Abstract

In this paper, the concept of adding learn-able slope and mean shift parameters to an activation function to improve the total response region is explored. The characteristics of an activation function depend highly on the value of parameters. Making the parameters learn-able, makes the activation function more dynamic and capable to adapt as per the requirements of it’s neighboring layers. The introduced slope parameter is independent of other parameters in the activation function. The concept was applied to ReLU to develop Dual Line and Dual Parametric ReLU activation function. Evaluation on MNIST and CIFAR10 show that the proposed activation function Dual Line achieves top-5 position for mean accuracy among 43 activation functions tested with LENET4, LENET5 and WideResNet architectures. This is the first time more than 40 activation functions were analyzed on MNIST and CIFAR10 dataset at the same time. The study on the distribution of positive slope parameter \(\beta \) indicates that the activation function adapts as per the requirements of the neighboring layers. The study shows that model performance increases with the proposed activation functions.