The role of deep transfer learning in modern machine learning systems

Abstract

Deep transfer learning (DTL) has emerged as a transformative approach within the field of machine learning, enabling efficient adaptation of pre-trained models to new tasks with limited labeled data. This review article explores the foundational concepts, applications, benefits, and challenges of DTL, emphasizing its role in advancing various domains such as computer vision, natural language processing, healthcare, and mechanical systems diagnosis. We delve into detailed methodologies, including fine-tuning, feature extraction, and domain adaptation, providing comprehensive insights into the mechanisms and strategies underpinning successful knowledge transfer.