  Past few years have witnessed exponential growth of interest in deep learning
methodologies with rapidly improving accuracies and reduced computational
complexity. In particular, architectures using Convolutional Neural Networks
(CNNs) have produced state-of-the-art performances for image classification and
object recognition tasks. Recently, Capsule Networks (CapsNet) achieved
significant increase in performance by addressing an inherent limitation of
CNNs in encoding pose and deformation. Inspired by such advancement, we asked
ourselves, can we do better? We propose Dense Capsule Networks (DCNet) and
Diverse Capsule Networks (DCNet++). The two proposed frameworks customize the
CapsNet by replacing the standard convolutional layers with densely connected
convolutions. This helps in incorporating feature maps learned by different
layers in forming the primary capsules. DCNet, essentially adds a deeper
convolution network, which leads to learning of discriminative feature maps.
Additionally, DCNet++ uses a hierarchical architecture to learn capsules that
represent spatial information in a fine-to-coarser manner, which makes it more
efficient for learning complex data. Experiments on image classification task
using benchmark datasets demonstrate the efficacy of the proposed
architectures. DCNet achieves state-of-the-art performance (99.75%) on MNIST
dataset with twenty fold decrease in total training iterations, over the
conventional CapsNet. Furthermore, DCNet++ performs better than CapsNet on SVHN
dataset (96.90%), and outperforms the ensemble of seven CapsNet models on
CIFAR-10 by 0.31% with seven fold decrease in number of parameters.
