  A capsule is a group of neurons whose activity vector models different
properties of the same entity. This paper extends the capsule to a generative
version, named variational capsules (VCs). Each VC produces a latent variable
for a specific entity, making it possible to integrate image analysis and image
synthesis into a unified framework. Variational capsules model an image as a
composition of entities in a probabilistic model. Different capsules'
divergence with a specific prior distribution represents the presence of
different entities, which can be applied in image analysis tasks such as
classification. In addition, variational capsules encode multiple entities in a
semantically-disentangling way. Diverse instantiations of capsules are related
to various properties of the same entity, making it easy to generate diverse
samples with fine-grained semantic attributes. Extensive experiments
demonstrate that deep networks designed with variational capsules can not only
achieve promising performance on image analysis tasks (including image
classification and attribute prediction) but can also improve the diversity and
controllability of image synthesis.
