Demystifying Capsule Networking
Lately, when Artificial Intelligence pioneer, Geoffrey Hinton, outlined an advance in the rate of accurate identification of images, Machine Learning leaped several steps in emulating the Brain’s Visual Processing capabilities. Considered to be the foundation of the commercial arm of Machine Learning, artificial neural networks banked on the least use of data, and thereby taking the name of Capsule Networking.
Wherein, the basic sense of Capsule Networking is to emulate human brain functioning and ‘learn’ from audio, images, and texts. While exploring this theory, unlike CNN, Capsule Networks are capable of establishing hierarchical relationships enabling the networks to consider spatial hierarchies. These spatial hierarchies between complex and simple objects prevent miscalculations and lower errors of possibility. These configurations have led organizations to seek Capsule Networks for deep reinforced learning and environmental interactions which are purposed to increase the overall field view of higher level ‘features pooling’.
There are varied problems with traditional Neural Networks. For instance, up until now, Conventional Neural networks (CNNs) have been using the state-of-the-art approach to classify images. It works by accumulating sets of features at each layer, first starting by finding edges, shapes and then the actual objects. However, the spatial relationship information of all these features gets lost.A CNN gets easily confused when viewing an image in a different orientation. One way to combat this is with an excessive training of all possible angles.