  We propose Pure CapsNets (P-CapsNets) which is a generation of normal CNNs
structurally. Specifically, we make three modifications to current CapsNets.
First, we remove routing procedures from CapsNets based on the observation that
the coupling coefficients can be learned implicitly. Second, we replace the
convolutional layers in CapsNets to improve efficiency. Third, we package the
capsules into rank-3 tensors to further improve efficiency. The experiment
shows that P-CapsNets achieve better performance than CapsNets with varied
routing procedures by using significantly fewer parameters on MNIST\&CIFAR10.
The high efficiency of P-CapsNets is even comparable to some deep compressing
models. For example, we achieve more than 99\% percent accuracy on MNIST by
using only 3888 parameters. We visualize the capsules as well as the
corresponding correlation matrix to show a possible way of initializing
CapsNets in the future. We also explore the adversarial robustness of
P-CapsNets compared to CNNs.
