Capsule Networks consist of groups of neurons called capsules that work together to recognize specific features and their spatial relationships. Unlike traditional neural networks, which can struggle with recognizing rotated or distorted images, capsule networks use a hierarchical structure to capture the part-whole relationships in data. This allows them to better understand complex patterns and variations, making them particularly effective for image recognition tasks.
Capsule Network Example
For example, in a capsule network trained to recognize objects, a capsule might identify a specific feature like an eye in an image of a face. If the face is rotated, the network can still recognize the eye’s position relative to the whole face, enabling it to identify the face correctly regardless of orientation.