Towards the Characterization of Representations Learned via
  Capsule-based Network Architectures
Saja AL-Tawalbeh José Oramas
Capsule Networks (CapsNets) have been re-introduced as a more compact and
interpretable alternative to standard deep neural networks. While recent
efforts have proved their compression capabilities, to date, their
interpretability properties have not been fully assessed. Here, we conduct a
systematic and principled study towards assessing the interpretability of these
types of networks. Moreover, we pay special attention towards analyzing the
level to which part-whole relationships are indeed encoded within the learned
representation. Our analysis in the MNIST, SVHN, PASCAL-part and CelebA
datasets suggest that the representations encoded in CapsNets might not be as
disentangled nor strictly related to parts-whole relationships as is commonly
stated in the literature.