A Novel Transfer Learning Framework for Enhancing Model Adaptability Across Domains
Abstract
Transfer learning has emerged as a powerful technique for improving model performance with limited training data. This paper proposes a novel transfer learning framework that combines domain adaptation strategies with self-supervised pretraining. We compare our approach against conventional transfer learning techniques across multiple domains, including healthcare diagnostics, financial forecasting, and natural language processing. The results demonstrate that our framework improves model adaptability and reduces the need for extensive labeled data, making AI more efficient for real-world applications.
References
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