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.

翻译：Capsule 网络在过去10年中表现出了巨大的进步, 超过传统的CNN在各种任务中的成绩, 因为它具有等同性特性。 由于使用矢量 I/ O, 提供一个对象或它部分的大小和方向的信息, 因此极有可能在无人监督的学习环境中使用 Capsule 网络来进行多级图像分类等视觉演示任务。 在本文中, 我们提议了 对抗性 Cepule (CoCa) 模式, 这是一种Siales 风格的 Capsule 网络, 使用与我们的新结构、 培训和测试算法的对比性损失 。 我们评估了未经监督的图像分类 CIFAR- 10 数据集的模型, 并实现了70.50% 和98. 10% 的顶层测试精度。 由于我们高效的架构, 我们的模型的参数比当前SOTA在受监管的学习和不超强的学习中比目前的SOTA少31倍, 71 倍 FLOPs 。