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.

--Capsule networks are a recently developed class of neural networks that potentially address some of the deficiencies with traditional convolutional neural networks. By replacing the standard scalar activations with vectors, and by connecting the artificial neurons in a new way, capsule networks aim to be the next great development for computer vision applications. However, in order to determine whether these networks truly operate differently than traditional networks, one must look at the differences in the capsule features. T o this end, we perform several analyses with the purpose of elucidating capsule features and determining whether they perform as described in the initial publication. First, we perform a deep visualization analysis to visually compare capsule features and convolutional neural network features. Then, we look at the ability for capsule features to encode information across the vector components and address what changes in the capsule architecture provides the most benefit. Finally, we look at how well the capsule features are able to encode instantiation parameters of class objects via visual transformations. ONVOLUTIONAL neural networks (CNNs) have long been the tools of choice when tackling computer vision problems. The spatial localization of CNN features is greatly beneficial when the networks are applied to images and videos; however, these networks also have their shortcomings. The kernels in a convolutional layer must learn to identify the presence of all relevant features in the input. Thus, transformations such as rotations and occlusion can be detrimental when the training dataset is not properly augmented. Even still, the burden of learning visual features in addition to all possible modifications of these features can be immense for a traditional CNN. Recently, a novel class of neural networks was proposed in [1] that employs the concept of a "capsule".