Capsule Deep Generative Model That Forms Parse Trees

Supervised capsule networks are theoretically advantageous over convolutional neural networks, because they aim to model a range of transformations of local physical or abstract objects and part-whole relationships among them. However, it remains unclear how to use the concept of capsules in deep generative models. In this study, to address this challenge, we present a statistical modelling of capsules in deep generative models where distributions are formulated in the exponential family. The major contribution of this unsupervised method is that parse trees as representations of part-whole relationships can be dynamically learned from the data.