A Comprehensive Survey on Transfer Learning: Methods and Applications

Abstract

Transfer learning has emerged as a pivotal area in machine learning, facilitating the transfer of knowledge from one domain to another, thereby enhancing performance, reducing training time, and addressing challenges related to data scarcity. This survey comprehensively explores the foundational theories, key methodologies, and diverse applications of transfer learning across various fields. We categorize transfer learning methods into feature-based, model-based, and instance-based approaches, elucidating the strengths and limitations of each. The survey also highlights advancements in deep learning techniques that have revolutionized transfer learning applications in areas such as natural language processing, computer vision, healthcare, and robotics. In addition, we discuss emerging trends, challenges, and future directions for research in transfer learning, providing insights for practitioners and researchers alike.