To address these issues related to complexity and information loss, we propose a family of quaternion-valued generative adversarial networks (QGANs). QGANs exploit the properties of quaternion algebra, e.g., the Hamilton product, that allows to process channels as a single entity and capture ...
Quaternionized versions of standard (real-valued) neural network layers have shown to lead to networks that are sparse and as effective as their real-valued counterparts. In this work, we explore their usefulness in the context of the Keyword Spotting task. Tests on a collection of manuscripts ...
In this work, we introduce and discuss Quaternion Generative Adversarial Networks, a variant of generative adversarial networks that uses quaternion-valued inputs, weights and intermediate network representations. Quaternionic representation has the advantage of treating cross-channel information carried by ...