  From the moment Neural Networks dominated the scene for image processing, the
computational complexity needed to solve the targeted tasks skyrocketed:
against such an unsustainable trend, many strategies have been developed,
ambitiously targeting performance's preservation. Promoting sparse topologies,
for example, allows the deployment of deep neural networks models on embedded,
resource-constrained devices. Recently, Capsule Networks were introduced to
enhance explainability of a model, where each capsule is an explicit
representation of an object or its parts. These models show promising results
on toy datasets, but their low scalability prevents deployment on more complex
tasks. In this work, we explore sparsity besides capsule representations to
improve their computational efficiency by reducing the number of capsules. We
show how pruning with Capsule Network achieves high generalization with less
memory requirements, computational effort, and inference and training time.
