Capsule networks with non-iterative cluster routing

TL;DR

This paper introduces a non-iterative cluster routing method for capsule networks that improves accuracy, reduces parameters, and enhances spatial and viewpoint generalization compared to existing routing algorithms.

Contribution

It proposes a novel non-iterative cluster routing mechanism that replaces traditional vote prediction with vote clusters and centroid-based routing in capsule networks.

Findings

Achieves state-of-the-art accuracy on Fashion-MNIST and SVHN with fewer parameters.

Attains top results on smallNORB and CIFAR-10 with moderate parameters.

Produces disentangled representations and maintains spatial information in capsules.

Abstract

Capsule networks use routing algorithms to flow information between consecutive layers. In the existing routing procedures, capsules produce predictions (termed votes) for capsules of the next layer. In a nutshell, the next-layer capsule's input is a weighted sum over all the votes it receives. In this paper, we propose non-iterative cluster routing for capsule networks. In the proposed cluster routing, capsules produce vote clusters instead of individual votes for next-layer capsules, and each vote cluster sends its centroid to a next-layer capsule. Generally speaking, the next-layer capsule's input is a weighted sum over the centroid of each vote cluster it receives. The centroid that comes from a cluster with a smaller variance is assigned a larger weight in the weighted sum process. Compared with the state-of-the-art capsule networks, the proposed capsule networks achieve the best accuracy on the Fashion-MNIST and SVHN datasets with fewer parameters, and achieve the best accuracy on the smallNORB and CIFAR-10 datasets with a moderate number of parameters. The proposed capsule networks also produce capsules with disentangled representation and generalize well to images captured at novel viewpoints. The proposed capsule networks also preserve 2D spatial information of an input image in the capsule channels: if the capsule channels are rotated, the object reconstructed from these channels will be rotated by the same transformation. Codes are available at https://github.com/ZHAOZHIHAO/ClusterRouting.