  Capsule Networks, as alternatives to Convolutional Neural Networks, have been
proposed to recognize objects from images. The current literature demonstrates
many advantages of CapsNets over CNNs. However, how to create explanations for
individual classifications of CapsNets has not been well explored. The widely
used saliency methods are mainly proposed for explaining CNN-based
classifications; they create saliency map explanations by combining activation
values and the corresponding gradients, e.g., Grad-CAM. These saliency methods
require a specific architecture of the underlying classifiers and cannot be
trivially applied to CapsNets due to the iterative routing mechanism therein.
To overcome the lack of interpretability, we can either propose new post-hoc
interpretation methods for CapsNets or modifying the model to have build-in
explanations. In this work, we explore the latter. Specifically, we propose
interpretable Graph Capsule Networks (GraCapsNets), where we replace the
routing part with a multi-head attention-based Graph Pooling approach. In the
proposed model, individual classification explanations can be created
effectively and efficiently. Our model also demonstrates some unexpected
benefits, even though it replaces the fundamental part of CapsNets. Our
GraCapsNets achieve better classification performance with fewer parameters and
better adversarial robustness, when compared to CapsNets. Besides, GraCapsNets
also keep other advantages of CapsNets, namely, disentangled representations
and affine transformation robustness.
