  Capsule networks offer interesting properties and provide an alternative to
today's deep neural network architectures. However, recent approaches have
failed to consistently achieve competitive results across different image
datasets. We propose a new parameter efficient capsule architecture, that is
able to tackle complex tasks by using neural networks trained with an
approximate Wasserstein objective to dynamically select capsules throughout the
entire architecture. This approach focuses on implementing a robust routing
scheme, which can deliver improved results using little overhead. We perform
several ablation studies verifying the proposed concepts and show that our
network is able to substantially outperform other capsule approaches by over
1.2 % on CIFAR-10, using fewer parameters.

Machine Learning;Computer Vision and Pattern Recognition;Machine Learning