Wasserstein Embedding for Capsule Learning. (arXiv:2209.00232v1 [cs.CV])

Capsule networks (CapsNets) aim to parse images into a hierarchical component
structure that consists of objects, parts, and their relations. Despite their
potential, they are computationally expensive and pose a major drawback, which
limits utilizing these networks efficiently on more complex datasets. The
current CapsNet models only compare their performance with the capsule
baselines and do not perform at the same level as deep CNN-based models on
complicated tasks. This paper proposes an efficient way for learning capsules
that detect …

arxiv embedding learning