  In capsule networks, the routing algorithm connects capsules in consecutive
layers, enabling the upper-level capsules to learn higher-level concepts by
combining the concepts of the lower-level capsules. Capsule networks are known
to have a few advantages over conventional neural networks, including
robustness to 3D viewpoint changes and generalization capability. However, some
studies have reported negative experimental results. Nevertheless, the reason
for this phenomenon has not been analyzed yet. We empirically analyzed the
effect of five different routing algorithms. The experimental results show that
the routing algorithms do not behave as expected and often produce results that
are worse than simple baseline algorithms that assign the connection strengths
uniformly or randomly. We also show that, in most cases, the routing algorithms
do not change the classification result but polarize the link strengths, and
the polarization can be extreme when they continue to repeat without stopping.
In order to realize the true potential of the capsule network, it is essential
to develop an improved routing algorithm.
