  Capsule networks are recently proposed as an alternative to modern neural
network architectures. Neurons are replaced with capsule units that represent
specific features or entities with normalized vectors or matrices. The
activation of lower layer capsules affects the behavior of the following
capsules via routing links that are constructed during training via certain
routing algorithms. We discuss the routing-by-agreement scheme in dynamic
routing algorithm which, in certain cases, leads the networks away from
optimality. To obtain better and faster convergence, we propose a routing
algorithm that incorporates a regularized quadratic programming problem which
can be solved efficiently. Particularly, the proposed routing algorithm targets
directly on the discriminative power of class capsules making the correct
decision on input instances. We conduct experiments on MNIST, MNIST-Fashion,
and CIFAR-10 and show competitive classification results compared to existing
capsule networks.

Machine Learning;Computer Vision and Pattern Recognition