Transfer Learning for Cross-domain Adaptation: Investigating transfer learning techniques for adapting machine learning models from one domain to another

Abstract

Transfer learning has emerged as a powerful technique in machine learning, enabling the adaptation of models trained on one domain to perform well on a different but related domain. This paper explores the latest advancements in transfer learning for cross-domain adaptation. We discuss the challenges involved in transferring knowledge between domains and review state-of-the-art transfer learning algorithms and methodologies. We also present case studies and applications where transfer learning has been successfully applied for cross-domain adaptation. Our analysis highlights the effectiveness of transfer learning in addressing domain shift and improving model performance in various real-world scenarios.