A Comparative Study of Routing Methods in Capsule Networks

Abstract: Recently, the deep neural network structure caps-net was proposed by Sabouret al. [11]. Capsule networks are designed to learn relative geometry betweenthe features of a layer and the features of the next layer. The Capsule network’smain building blocks are capsules, which are represented by vectors. The ideais that each capsule will represent a feature as well as traits or subfeatures ofthat feature. This allows for smart information routing. Capsules traits are usedto predict the traits of the capsules in the next layer, and information is sent toto next layer capsules on which the predictions agree. This is called routing byagreement.This thesis investigates theoretical support of new and existing routing al-gorithms as well as evaluates their performance on the MNIST [16] and CIFAR-10 [8] datasets. A variation of the dynamic routing algorithm presented in theoriginal paper [11] achieved the highest accuracy and fastest execution time.