Capsule Network

In computer science, a capsule network is a neural network architecture that uses capsules to group together related features and make predictions about the underlying objects. Capsules are groups of neurons that share common weights and produce a vector output. This vector output encodes information about the object's properties, such as its position, size, orientation, and velocity. The capsules in a layer are interconnected, and the layers are stacked on top of each other like a neural network. The capsules in each layer make predictions about the capsules in the next layer. The predictions are made by multiplying the capsule's output vector by a weight matrix. The weight matrix is learned during training. The goal of training is to minimize the error between the predicted outputs and the actual outputs. Capsules can be used for various tasks, such as image classification, object detection, and activity recognition.