  Capsule Network is a promising concept in deep learning, yet its true
potential is not fully realized thus far, providing sub-par performance on
several key benchmark datasets with complex data. Drawing intuition from the
success achieved by Convolutional Neural Networks (CNNs) by going deeper, we
introduce DeepCaps1, a deep capsule network architecture which uses a novel 3D
convolution based dynamic routing algorithm. With DeepCaps, we surpass the
state-of-the-art results in the capsule network domain on CIFAR10, SVHN and
Fashion MNIST, while achieving a 68% reduction in the number of parameters.
Further, we propose a class-independent decoder network, which strengthens the
use of reconstruction loss as a regularization term. This leads to an
interesting property of the decoder, which allows us to identify and control
the physical attributes of the images represented by the instantiation
parameters.
