Resumen
Classifying digital images using neural networks is one of the most fundamental tasks within the field of artificial intelligence. For a long time, convolutional neural networks have proven to be the most efficient solution for processing visual data, such as classification, detection, or segmentation. The efficient operation of convolutional neural networks requires the use of data augmentation and a high number of feature maps to embed object transformations. Especially for large datasets, this approach is not very efficient. In 2017, Geoffrey Hinton and his research team introduced the theory of capsule networks. Capsule networks offer a solution to the problems of convolutional neural networks. In this approach, sufficient efficiency can be achieved without large-scale data augmentation. However, the training time for Hinton?s capsule network is much longer than for convolutional neural networks. We have examined the capsule networks and propose a modification in the routing mechanism to speed up the algorithm. This could reduce the training time of capsule networks by almost half in some cases. Moreover, our solution achieves performance improvements in the field of image classification.