The Impact of Transfer Learning on AI Performance Across Domains

Abstract

This study investigates the profound impact of transfer learning on the performance of artificial intelligence (AI) models when applied across diverse domains. Transfer learning, a machine learning technique that leverages knowledge gained from one task to improve performance on a related task, has demonstrated remarkable success in various applications. The article explores the underlying principles of transfer learning, its mechanisms, and the ways in which it enhances AI performance. The findings highlight the potential of transfer learning to facilitate knowledge transfer between domains, reduce training data requirements, and accelerate model convergence, ultimately contributing to the broader adaptability and efficiency of AI systems