Parametric Flatten-T Swish: An Adaptive Non-linear Activation Function For Deep Learning

TL;DR

This paper introduces Parametric Flatten-T Swish (PFTS), an adaptive activation function designed to overcome ReLU's limitations, leading to improved training efficiency and classification accuracy in deep neural networks.

Contribution

The paper proposes PFTS, a novel adaptive activation function that enhances non-linear approximation, flexibility, and robustness compared to ReLU, with demonstrated performance gains.

Findings

PFTS improves classification accuracy on SVHN dataset across various DNN architectures.

PFTS achieves higher mean rank among compared activation functions.

PFTS demonstrates increased non-linear approximation power during training.

Abstract

Activation function is a key component in deep learning that performs non-linear mappings between the inputs and outputs. Rectified Linear Unit (ReLU) has been the most popular activation function across the deep learning community. However, ReLU contains several shortcomings that can result in inefficient training of the deep neural networks, these are: 1) the negative cancellation property of ReLU tends to treat negative inputs as unimportant information for the learning, resulting in a performance degradation; 2) the inherent predefined nature of ReLU is unlikely to promote additional flexibility, expressivity, and robustness to the networks; 3) the mean activation of ReLU is highly positive and leads to bias shift effect in network layers; and 4) the multilinear structure of ReLU restricts the non-linear approximation power of the networks. To tackle these shortcomings, this paper introduced Parametric Flatten-T Swish (PFTS) as an alternative to ReLU. By taking ReLU as a baseline method, the experiments showed that PFTS improved classification accuracy on SVHN dataset by 0.31%, 0.98%, 2.16%, 17.72%, 1.35%, 0.97%, 39.99%, and 71.83% on DNN-3A, DNN-3B, DNN-4, DNN- 5A, DNN-5B, DNN-5C, DNN-6, and DNN-7, respectively. Besides, PFTS also achieved the highest mean rank among the comparison methods. The proposed PFTS manifested higher non-linear approximation power during training and thereby improved the predictive performance of the networks.