Transfer Learning Strategies in Deep Learning: Analyzing transfer learning strategies to transfer knowledge from pre-trained models to new tasks with limited labeled data

Abstract

Transfer learning has emerged as a powerful approach in deep learning, allowing models to leverage knowledge from pre-trained models to improve performance on new tasks with limited labeled data. This paper provides a comprehensive analysis of transfer learning strategies in deep learning, focusing on techniques such as fine-tuning, feature extraction, and domain adaptation. We discuss the underlying principles of transfer learning and its applications in various domains. Additionally, we explore the challenges and limitations of transfer learning, including domain shift and negative transfer, and propose potential solutions. Through experiments and case studies, we demonstrate the effectiveness of transfer learning strategies in improving model performance and reducing the need for large labeled datasets. Overall, this paper aims to provide insights into the current state of transfer learning in deep learning and its future directions.