  Capsules are grouping of neurons that allow to represent sophisticated
information of a visual entity such as pose and features. In the view of this
property, Capsule Networks outperform CNNs in challenging tasks like object
recognition in unseen viewpoints, and this is achieved by learning the
transformations between the object and its parts with the help of high
dimensional representation of pose information. In this paper, we present
Quaternion Capsules (QCN) where pose information of capsules and their
transformations are represented by quaternions. Quaternions are immune to the
gimbal lock, have straightforward regularization of the rotation representation
for capsules, and require less number of parameters than matrices. The
experimental results show that QCNs generalize better to novel viewpoints with
fewer parameters, and also achieve on-par or better performances with the
state-of-the-art Capsule architectures on well-known benchmarking datasets.
