  A capsule is a collection of neurons which represents different variants of a
pattern in the network. The routing scheme ensures only certain capsules which
resemble lower counterparts in the higher layer should be activated. However,
the computational complexity becomes a bottleneck for scaling up to larger
networks, as lower capsules need to correspond to each and every higher
capsule. To resolve this limitation, we approximate the routing process with
two branches: a master branch which collects primary information from its
direct contact in the lower layer and an aide branch that replenishes master
based on pattern variants encoded in other lower capsules. Compared with
previous iterative and unsupervised routing scheme, these two branches are
communicated in a fast, supervised and one-time pass fashion. The complexity
and runtime of the model are therefore decreased by a large margin. Motivated
by the routing to make higher capsule have agreement with lower capsule, we
extend the mechanism as a compensation for the rapid loss of information in
nearby layers. We devise a feedback agreement unit to send back higher capsules
as feedback. It could be regarded as an additional regularization to the
network. The feedback agreement is achieved by comparing the optimal transport
divergence between two distributions (lower and higher capsules). Such an
add-on witnesses a unanimous gain in both capsule and vanilla networks. Our
proposed EncapNet performs favorably better against previous state-of-the-arts
on CIFAR10/100, SVHN and a subset of ImageNet.

Machine Learning;Computer Vision and Pattern Recognition;Machine Learning