Transfer Learning in Data Science: Reviewing transfer learning techniques for transferring knowledge from one domain to another to improve model performance

Abstract

Transfer learning has emerged as a powerful technique in data science for improving model performance by leveraging knowledge from one domain to another. This paper provides a comprehensive review of transfer learning techniques, focusing on their applications, advantages, and challenges in various data science tasks. The paper begins by defining transfer learning and its importance in addressing the limitations of traditional machine learning approaches. It then discusses different types of transfer learning, such as domain adaptation, multitask learning, and sequential transfer. The paper also examines the underlying principles of transfer learning, including feature representation, model adaptation, and knowledge transfer mechanisms. Additionally, the paper explores recent advancements in transfer learning, such as deep transfer learning and meta-learning. The paper concludes with a discussion on future directions and open research challenges in transfer learning.