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.

本文提出了一种新型的 Capsule 网络模型：基于对比损失的 Siamese 风格 Capsule 网络模型 CoCa，将其应用于无监督形象分类任务，如 CIFAR-10 数据集，达到了较高的准确度并在模型参数和计算复杂度上均优于现有的有监督和无监督模型。