Around the clock ‐ capsule networks and image transformations

Abstract Capsule networks are a promising new model class for image processing tasks. We investigate and compare their classification performance with convolutional neural networks on a new entry level image data set. In our experiments the capsules are able to learn the effect of rotated input images from data and outperform a comparable convolutional architecture. The results also show that convolutional and capsule networks need structural adjustment to respond to transformations that are not included in the training set.