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…
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