CapsNets are very different from state-of-the-art deep learning networks. Instead of adding more layers and making the network deeper, CapsNets use a shallow network where capsule layers are nested inside other layers. Each capsule specializes in detecting a specific entity in an image, and a dynamic routing mechanism is used to send the detected entity to parents layers. With CNNs you have to consider thousands of images from many different perspectives in order to recognize an object from different angles. Hinton believes the redundancies in the layers will allow capsule networks to identify objects from multiple angles and in different scenarios with less data that is typically used by CNNs. Let's examine the network as ...
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