论文标题
基于胶囊网络的对比度学习无监督的视觉表示
Capsule Network based Contrastive Learning of Unsupervised Visual Representations
论文作者
论文摘要
在过去的十年中，胶囊网络已显示出巨大的进步，由于其模棱两可的属性，在各种任务中表现出色。通过使用向量I/O提供对象的幅度和方向的信息，或者是该部分的一部分，在无监督的学习环境中使用胶囊网络来进行视觉表示任务，例如多类图像分类。在本文中，我们提出了对比度胶囊（可口可乐）模型，该模型是一种暹罗风格的胶囊网络，使用我们的新型体系结构，训练和测试算法使用对比度损失。我们评估了无监督图像分类CIFAR-10数据集的模型，并获得了70.50％的TOP-1测试精度，前5个测试精度为98.10％。由于我们有效的体系结构，我们的模型的参数少了31倍，而在受监督和无监督学习中的参数却比当前的SOTA少了71倍。
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.
