capsule network

Capsule Network, also known as CapsNet, is a type of neural network architecture that aims to overcome the limitations of traditional convolutional neural networks (CNNs) in image recognition tasks. CapsNet was proposed by Geoffrey Hinton and his team in 2017.

In a CapsNet, the basic building block is called a capsule, which is a group of neurons that represent a specific object or part of an object in an image. Capsules are arranged in layers, with each layer representing a level of abstraction in the image. The output of each capsule in one layer is used as input to the capsules in the next layer.

One of the key features of CapsNet is the use of vector outputs for the capsules, as opposed to scalar outputs used in CNNs. Vector outputs allow for more accurate and robust representation of objects, especially when dealing with variations in viewpoints, lighting, and other factors.

CapsNet is still a relatively new and experimental architecture, and its performance is still being evaluated and improved upon. However, it has shown promising results in some image recognition tasks, and may become an important tool in the field of computer vision.