Transfer learning is a technique that leverages knowledge gained from a pre-trained model to solve a new but related problem. Instead of training a model from scratch, transfer learning uses a model already trained on (ideally) a large and similar dataset to transfer its learned features to a different domain and/or task, often requiring significantly less data and computational resources.
For example, a model trained to recognize objects in general images (like dogs, cars, and trees) can be adapted to identify specific types of medical abnormalities in X-rays. This approach reduces the need for large, labeled datasets, which can be expensive and time-consuming to create, and allows researchers and developers to build effective models more efficiently.
