Adapting Models to New Domains to comply with Transfer Learning Strategies

Abstract

The advent of transfer learning has significantly advanced the ability of machine learning models to adapt to new domains with limited data. This paper, titled "Adapting Models to New Domains to Comply with Transfer Learning Strategies," explores the methodologies and best practices for effectively transitioning models trained in one domain to perform well in a new, but related domain. We provide a comprehensive review of various transfer learning strategies, including domain adaptation, fine-tuning, and multi-task learning, highlighting their applications and limitations. The paper also presents a detailed analysis of the challenges involved in domain adaptation, such as data distribution shifts feature misalignment, and model overfitting. Through a series of case studies and experimental results, we demonstrate the effectiveness of different strategies in enhancing model performance across diverse domains. Finally, we propose a framework for selecting and implementing appropriate transfer learning techniques based on the specific characteristics of the target domain. This work aims to provide practical insights and guidance for researchers and practitioners seeking to leverage transfer learning to improve model generalization and applicability in novel settings.