Learn in the textbookTransfer of Learning and Representative Deep Learning Models
DefinitionTransfer learning (転移学習) is a method that carries the knowledge of a model trained on one task over to a related, different task, so that learning the new task goes efficiently.
ExplanationFor example, an image recognition model trained to tell many kinds of objects apart can be put to a different task: spotting scratches on products in a factory. Even with little data for the new task, it can draw on the shape and pattern features it has already learned, so it tends to reach good accuracy in less time than training from scratch. Fine-tuning (ファインチューニング) with a trained model as the base is one of the main ways to achieve transfer learning.
AnalogyIt is like someone who has learned the piano using their feel for the keys and their ability to read music to pick up the electronic organ quickly.
Memory hookIn English it is transfer learning. "Transfer" is the same word used for changing trains, so you can picture knowledge switching over to another line.