Keywords
machine learning adaptation
domain adaptation
fine-tuning
multi-task learning
model generalization
model robustness
knowledge transfer
adaptation techniques
cross-domain learning
How to Cite
Abstract
Transfer learning, a subfield of machine learning, has gained significant attention for its ability to leverage knowledge from one domain to improve learning in another domain. This paper presents a systematic exploration of transfer learning techniques aimed at adapting machine learning models across disparate domains. We delve into various methodologies, including fine-tuning, domain adaptation, and multi-task learning, to elucidate their efficacy in bridging domains. Through comprehensive experimentation and analysis, we investigate the impact of transfer learning on model performance, generalization, and robustness across diverse domains. Our findings shed light on the nuanced intricacies of transfer learning and offer insights into selecting appropriate techniques for specific adaptation scenarios. Furthermore, we discuss challenges and future directions to advance the field of transfer learning and its application across various domains.
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