  Capsule networks are a type of neural network that identify image parts and
form the instantiation parameters of a whole hierarchically. The goal behind
the network is to perform an inverse computer graphics task, and the network
parameters are the mapping weights that transform parts into a whole. The
trainability of capsule networks in complex data with high intra-class or
intra-part variation is challenging. This paper presents a multi-prototype
architecture for guiding capsule networks to represent the variations in the
image parts. To this end, instead of considering a single capsule for each
class and part, the proposed method employs several capsules (co-group
capsules), capturing multiple prototypes of an object. In the final layer,
co-group capsules compete, and their soft output is considered the target for a
competitive cross-entropy loss. Moreover, in the middle layers, the most active
capsules map to the next layer with a shared weight among the co-groups.
Consequently, due to the reduction in parameters, implicit weight-sharing makes
it possible to have more deep capsule network layers. The experimental results
on MNIST, SVHN, C-Cube, CEDAR, MCYT, and UTSig datasets reveal that the
proposed model outperforms others regarding image classification accuracy.
