  Capsule networks have gained a lot of popularity in short time due to its
unique approach to model equivariant class specific properties as capsules from
images. However the dynamic routing algorithm comes with a steep computational
complexity. In the proposed approach we aim to create scalable versions of the
capsule networks that are much faster and provide better accuracy in problems
with higher number of classes. By using dynamic routing to extract intermediate
features instead of generating output class specific capsules, a large increase
in the computational speed has been observed. Moreover, by extracting
equivariant feature capsules instead of class specific capsules, the
generalization capability of the network has also increased as a result of
which there is a boost in accuracy.
