  Convolutional neural networks (CNNs) have revolutionized the field of deep
neural networks. However, recent research has shown that CNNs fail to
generalize under various conditions and hence the idea of capsules was
introduced in 2011, though the real surge of research started from 2017. In
this paper, we present an overview of the recent advances in capsule
architecture and routing mechanisms. In addition, we find that the relative
focus in recent literature is on modifying routing procedure or architecture as
a whole but the study of other finer components, specifically, squash function
is wanting. Thus, we also present some new insights regarding the effect of
squash functions in performance of the capsule networks. Finally, we conclude
by discussing and proposing possible opportunities in the field of capsule
networks.
