What are Capsule Networks?

Before diving deep into capsule network, there is a necessity to know what is a capsule and what is the need capsule network as Convolutional Neural Network (CNN) already have existence for dealing with the same kind of the task. A capsule is a combination of neurons whose performance vector signifies the creation of real instance parameters of a particular type of an object or it’s part. The predictions are made using matrices which are a transformation in nature, for a real instance parameter which belong the upper-level capsules.

The output of the capsules (which are at the lower level) is sent to the other capsules (which are at the higher level), the performance vectors of these capsules are calculated by a significant nature of the scalar product and the results as predictions coming from the capsules at a lower level.

The problem with CNN is that they can not consider the orientation of the object which is demanded to be detected in a particular task. So if an image had eyes, nose, lips, and ears but placed anywhere in the image, then it will be detected as a face. That is the reason why capsule networks come into the play as they have the power to identify the object with their specific orientation.

How Capsule Networks Work?

There are two main concepts on which Capsule Networks works –

The first concept is the “Representation of Multidimensional Entities.” This is handled creating a feature from grouping these properties.

The second concept is “initializing the features which are at higher-level with the help of a concession between features which are at lower-level.” This is also known as “routing by agreement.”

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