(untitled) (Journal of Artificial Intelligence & Research, 2024)
Transfer learning has emerged as a powerful technique in the field of artificial intelligence (AI), allowing models to leverage knowledge from one domain or task to improve performance on another. This paper provides a comprehensive analysis of transfer learning strategies in AI applications, focusing on their effectiveness in adapting models to new tasks and domains. We review prominent transfer learning approaches, including feature extraction, fine-tuning, and domain adaptation, highlighting their strengths and limitations. Furthermore, we discuss recent advancements in transfer learning, such as meta-learning and continual learning, and their implications for AI applications. Through a series of experiments and case studies, we evaluate the performance of transfer learning strategies across different datasets and domains, shedding light on best practices and challenges in their implementation. Our findings suggest that transfer learning can significantly improve the efficiency and effectiveness of AI models, particularly in scenarios with limited labeled data or domain shifts. We conclude with recommendations for future research directions to further enhance the applicability of transfer learning in AI applications.