  We propose a novel capsule network based variational encoder architecture,
called Bayesian capsules (B-Caps), to modulate the mean and standard deviation
of the sampling distribution in the latent space. We hypothesized that this
approach can learn a better representation of features in the latent space than
traditional approaches. Our hypothesis was tested by using the learned latent
variables for image reconstruction task, where for MNIST and Fashion-MNIST
datasets, different classes were separated successfully in the latent space
using our proposed model. Our experimental results have shown improved
reconstruction and classification performances for both datasets adding
credence to our hypothesis. We also showed that by increasing the latent space
dimension, the proposed B-Caps was able to learn a better representation when
compared to the traditional variational auto-encoders (VAE). Hence our results
indicate the strength of capsule networks in representation learning which has
never been examined under the VAE settings before.
