  In this work, we investigate the following: 1) how the routing affects the
CapsNet model fitting; 2) how the representation using capsules helps discover
global structures in data distribution, and; 3) how the learned data
representation adapts and generalizes to new tasks. Our investigation yielded
the results some of which have been mentioned in the original paper of CapsNet,
they are: 1) the routing operation determines the certainty with which a layer
of capsules pass information to the layer above and the appropriate level of
certainty is related to the model fitness; 2) in a designed experiment using
data with a known 2D structure, capsule representations enable a more
meaningful 2D manifold embedding than neurons do in a standard convolutional
neural network (CNN), and; 3) compared with neurons of the standard CNN,
capsules of successive layers are less coupled and more adaptive to new data
distribution.
