H-CapsNet: A capsule network for hierarchical image classification

Abstract

In this paper, we present H-CapsNet, a capsule network for hierarchical image classification. Our network makes use of the natural capacity of CapsNets (capsule networks) to capture hierarchical relationships. Thus, our network is such that each multi-layer capsule network accounts for each of the class hierarchies using dedicated capsules. Further, we make use of a modified hinge loss that enforces consistency amongst the hierarchies involved. We also present a strategy to dynamically adjust the training parameters to achieve a better balance between the class hierarchies under consideration. We have performed experiments using several widely available datasets and compared them against several alternatives. In our experiments, H-CapsNet delivers a margin of improvement over competing hierarchical classification networks elsewhere in the literature.