Transfer Learning for Cross-Domain Adaptation in Image Classification

Abstract

Transfer learning has emerged as a powerful technique for improving the performance of image classification models, particularly when dealing with cross-domain adaptation. This paper explores the principles and methodologies of transfer learning, focusing on how it can be applied to adapt models from one domain to another. We review various strategies and techniques for cross-domain adaptation, evaluate their effectiveness, and discuss the challenges and future directions in this field. Our findings indicate that transfer learning can significantly enhance image classification tasks across domains, but careful consideration is needed to address domain-specific challenges.