  We propose an interpretable Capsule Network, iCaps, for image classification.
A capsule is a group of neurons nested inside each layer, and the one in the
last layer is called a class capsule, which is a vector whose norm indicates a
predicted probability for the class. Using the class capsule, existing Capsule
Networks already provide some level of interpretability. However, there are two
limitations which degrade its interpretability: 1) the class capsule also
includes classification-irrelevant information, and 2) entities represented by
the class capsule overlap. In this work, we address these two limitations using
a novel class-supervised disentanglement algorithm and an additional
regularizer, respectively. Through quantitative and qualitative evaluations on
three datasets, we demonstrate that the resulting classifier, iCaps, provides a
prediction along with clear rationales behind it with no performance
degradation.
