The Role of Transfer Learning in Improving Model Performance with Limited Data

Abstract

In the realm of machine learning and deep learning, the scarcity of large labeled datasets continues to hinder the development of high-performing models, particularly in specialized domains such as medical imaging, remote sensing, and natural language processing for low-resource languages. Transfer learning emerges as a compelling solution by leveraging pre-trained models and adapting them to new, related tasks with limited data. This paper explores the theoretical underpinnings of transfer learning, practical strategies for its implementation, and case studies highlighting its effectiveness. Experimental results demonstrate that models utilizing transfer learning consistently outperform models trained from scratch, especially in low-data regimes. We conclude by discussing limitations, such as domain mismatch and negative transfer, and propose future research directions