  Deep convolutional neural networks, assisted by architectural design
strategies, make extensive use of data augmentation techniques and layers with
a high number of feature maps to embed object transformations. That is highly
inefficient and for large datasets implies a massive redundancy of features
detectors. Even though capsules networks are still in their infancy, they
constitute a promising solution to extend current convolutional networks and
endow artificial visual perception with a process to encode more efficiently
all feature affine transformations. Indeed, a properly working capsule network
should theoretically achieve higher results with a considerably lower number of
parameters count due to intrinsic capability to generalize to novel viewpoints.
Nevertheless, little attention has been given to this relevant aspect. In this
paper, we investigate the efficiency of capsule networks and, pushing their
capacity to the limits with an extreme architecture with barely 160K
parameters, we prove that the proposed architecture is still able to achieve
state-of-the-art results on three different datasets with only 2% of the
original CapsNet parameters. Moreover, we replace dynamic routing with a novel
non-iterative, highly parallelizable routing algorithm that can easily cope
with a reduced number of capsules. Extensive experimentation with other capsule
implementations has proved the effectiveness of our methodology and the
capability of capsule networks to efficiently embed visual representations more
prone to generalization.
