Capsule networks are a type of neural network architecture designed to improve the way computers recognize and process images by maintaining spatial hierarchies between features. This architecture uses groups of neurons, called capsules, that work together to identify specific features and their relationships in a way that mimics human perception, aiming to overcome some limitations of traditional convolutional neural networks (CNNs). By preserving the spatial orientation of features, capsule networks enhance the model's ability to generalize from fewer training examples and recognize objects regardless of their orientation or position.
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