Towards the Characterization of Representations Learned via Capsule-based Network Architectures
This work addresses the interpretability gap in Capsule Networks for researchers and practitioners, revealing limitations in their assumed properties.
The study systematically assessed the interpretability of Capsule Networks, finding that their learned representations are less disentangled and less strictly related to part-whole relationships than commonly claimed, based on analysis across MNIST, SVHN, PASCAL-part, and CelebA datasets.
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.