  Capsule Networks have shown tremendous advancement in the past decade,
outperforming the traditional CNNs in various task due to it's equivariant
properties. With the use of vector I/O which provides information of both
magnitude and direction of an object or it's part, there lies an enormous
possibility of using Capsule Networks in unsupervised learning environment for
visual representation tasks such as multi class image classification. In this
paper, we propose Contrastive Capsule (CoCa) Model which is a Siamese style
Capsule Network using Contrastive loss with our novel architecture, training
and testing algorithm. We evaluate the model on unsupervised image
classification CIFAR-10 dataset and achieve a top-1 test accuracy of 70.50% and
top-5 test accuracy of 98.10%. Due to our efficient architecture our model has
31 times less parameters and 71 times less FLOPs than the current SOTA in both
supervised and unsupervised learning.
