Quaternionic Convolutional Neural Networks with Trainable Bessel Activation Functions

Abstract

Quaternionic convolutional neural networks (QCNN) possess the ability to capture both external dependencies between neighboring features and internal latent dependencies within features of an input vector. In this study, we employ QCNN with activation functions based on Bessel-type functions with trainable parameters, for performing classification tasks. Our experimental results demonstrate that this activation function outperforms the traditional ReLU activation function. Throughout our simulations, we explore various network architectures. The use of activation functions with trainable parameters offers several advantages, including enhanced flexibility, adaptability, improved learning, customized model behavior, and automatic feature extraction.