The Role of Transfer Learning in Accelerating Model Training for Resource-Constrained Applications

Abstract

Transfer learning has emerged as a transformative approach in machine learning, enabling the reuse of pre-trained models to address resource-constrained applications effectively. By leveraging knowledge from large-scale datasets and computationally expensive training processes, transfer learning accelerates model development in domains with limited labeled data or computational resources. This technique has proven particularly impactful in areas such as healthcare, agriculture, and IoT, where data scarcity and device limitations pose significant challenges. This paper explores the mechanisms of transfer learning, including fine-tuning, feature extraction, and domain adaptation, and examines their applications across various resource-constrained scenarios. Furthermore, it highlights the benefits of reduced training times, lower computational costs, and improved model performance. Challenges such as negative transfer, domain mismatch, and optimization complexities are also discussed, along with potential strategies to address them. By analyzing case studies and advancements, this study provides insights into the role of transfer learning in democratizing access to advanced AI technologies.