  Capsule Networks (CapsNets) are brand-new architectures that have shown
ground-breaking results in certain areas of Computer Vision (CV). In 2017,
Hinton and his team introduced CapsNets with routing-by-agreement in "Sabour et
al" and in a more recent paper "Matrix Capsules with EM Routing" they proposed
a more complete architecture with Expectation-Maximization (EM) algorithm.
Unlike the traditional convolutional neural networks (CNNs), this architecture
is able to preserve the pose of the objects in the picture. Due to this
characteristic, it has been able to beat the previous state-of-theart results
on the smallNORB dataset, which includes samples with various view points.
Also, this architecture is more robust to white box adversarial attacks.
However, CapsNets have two major drawbacks. They can't perform as well as CNNs
on complex datasets and, they need a huge amount of time for training. We try
to mitigate these shortcomings by finding optimum settings of EM routing
iterations for training CapsNets. Unlike the past studies, we use un-equal
numbers of EM routing iterations for different stages of the CapsNet. For our
research, we use three datasets: Yale face dataset, Belgium Traffic Sign
dataset, and Fashion-MNIST dataset.
