Capsule Network

1. These capsules are particularly good at handling different types of visual stimulus and encoding things like pose (position, size, orientation), deformation, velocity, albedo, hue, texture etc.

2. Capsule is a nested set of neural layers. So in a regular neural network you keep on adding more layers. In CapsNet you would add more layers inside a single layer. Or in other words nest a neural layer inside another. The state of the neurons inside a capsule capture the above properties of one entity inside an image.

3. A capsule network has each capsule outputting a vector with the information about the position, scale, and rotation about the feature.
4. Each capsule in layer L has a coupling strength c with each capsule in layer L+1,
5. provide is taking a step to move from black-box neural networks to those that represent more concrete features that can help us analyze and understand what these are doing under the hood.

最后

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