disentangled representation learning

Disentangled Representation Learning is a way of teaching computers how to break down hard-to-understand data into simpler parts. This makes it easier for the computer to learn how to use the data more efficiently. It works by turning the data into 'clusters' where each cluster contains data that has something in common. For example, a cluster of pictures of cats might contain only pictures of cats, and a cluster of pictures of dogs might contain only pictures of dogs. By sorting the data this way, the computer can learn more easily from the data, since it has been made simpler.