What is Transfer Learning?
Transfer or inductive learning is a supervised learning technique that reuses parts of a previously trained model on a new network tasked for a different but similar problem. In computer vision, for example, some feature extractors from a nudity detection model could be used to speed up the learning process for a new facial recognition model.
How is Transfer Learning Used?
Using a pre-trained model significantly reduces the time required for feature engineering and training. The first step is to select a source model, ideally one with a large dataset to train with. Many research institutions release these models and datasets as open-sourced projects, so it’s not necessary to create your own.