Capsule Based Networks,
Capsule Based Networks, Next Evolution of Convolutional Neural Networks?
Deep convolutional neural networks (CNNs), assisted by architectural design strategies, make large use of data augmentation techniques and layers with a high number of feature maps to embed object transformations. That is highly inefficient and for large datasets implies a massive redundancy of features detectors. Even though capsule networks are still in their infancy, they constitute a promising solution to extend current convolutional networks and endow artificial visual perception with a process to encode more efficiently all feature affine transformations. Are they going to be the next evolutionary step for CNNs?