  Capsule Networks attempt to represent patterns in images in a way that
preserves hierarchical spatial relationships. Additionally, research has
demonstrated that these techniques may be robust against adversarial
perturbations. We present an improvement to training capsule networks with
added robustness via non-parametric kernel methods. The representations learned
through the capsule network are used to construct covariance kernels for
Gaussian processes (GPs). We demonstrate that this approach achieves comparable
prediction performance to Capsule Networks while improving robustness to
adversarial perturbations and providing a meaningful measure of uncertainty
that may aid in the detection of adversarial inputs.
