  Capsule networks are a class of neural networks that achieved promising
results on many computer vision tasks. However, baseline capsule networks have
failed to reach state-of-the-art results on more complex datasets due to the
high computation and memory requirements. We tackle this problem by proposing a
new network architecture, called Momentum Capsule Network (MoCapsNet).
MoCapsNets are inspired by Momentum ResNets, a type of network that applies
reversible residual building blocks. Reversible networks allow for
recalculating activations of the forward pass in the backpropagation algorithm,
so those memory requirements can be drastically reduced. In this paper, we
provide a framework on how invertible residual building blocks can be applied
to capsule networks. We will show that MoCapsNet beats the accuracy of baseline
capsule networks on MNIST, SVHN, CIFAR-10 and CIFAR-100 while using
considerably less memory. The source code is available on
https://github.com/moejoe95/MoCapsNet.
