  We present a framework for learning disentangled representation of CapsNet by
information bottleneck constraint that distills information into a compact form
and motivates to learn an interpretable factorized capsule. In our
$\beta$-CapsNet framework, hyperparameter $\beta$ is utilized to trade-off
disentanglement and other tasks, variational inference is utilized to convert
the information bottleneck term into a KL divergence that is approximated as a
constraint on the mean of the capsule. For supervised learning, class
independent mask vector is used for understanding the types of variations
synthetically irrespective of the image class, we carry out extensive
quantitative and qualitative experiments by tuning the parameter $\beta$ to
figure out the relationship between disentanglement, reconstruction and
classfication performance. Furthermore, the unsupervised $\beta$-CapsNet and
the corresponding dynamic routing algorithm is proposed for learning
disentangled capsule in an unsupervised manner, extensive empirical evaluations
suggest that our $\beta$-CapsNet achieves state-of-the-art disentanglement
performance compared to CapsNet and various baselines on several complex
datasets both in supervision and unsupervised scenes.
