Quaternion Valued Convolutional Neural Network

Quaternion-valued convolutional neural networks (QCNNs) leverage the properties of quaternions—a number system extending complex numbers—to process multi-dimensional data, such as color images and 3D sensor readings, more efficiently and effectively than traditional real-valued CNNs. Current research focuses on developing novel quaternion activation functions, incorporating quaternions into various architectures like generative adversarial networks (GANs) and recurrent neural networks (RNNs), and exploring their application in diverse fields including image inpainting, fault diagnosis, and speech emotion recognition. This approach shows promise in improving model performance, reducing parameter counts, and enhancing robustness, particularly in applications dealing with noisy or high-dimensional data.