Transfer learning is a machine learning paradigm focused on improving the learning performance on a target task by leveraging knowledge acquired from a related source task or domain. This methodological approach investigates principles and techniques for adapting models trained on one data distribution or task to another, thereby reducing the need for extensive labeled data or computational resources in the target domain and enhancing model generalizability and efficiency.