Transfer Learning: A New Promising Techniques
Ahmed Hussein Ali, Mohanad G. Yaseen, Mohammad Aljanabi, Saad Abbas Abed
Abstract
Transfer Learning is a machine learning technique that involves utilizing knowledge learned from one task to improve performance on another related task. This approach has been widely adopted in various fields such as computer vision, natural language processing, and speech recognition. The goal of this paper is to provide an overview of transfer learning and its recent developments. Transfer learning is particularly useful in situations where there is limited labeled data available for the target task. In these cases, the model can leverage knowledge learned from a related task with a larger amount of labeled data. This allows the model to overcome the problem of overfitting and improve performance on the target task.