Transfer Learning in AI: Techniques for transferring knowledge from one domain to another with minimal data
Gaurav Kashyap · Journal of Artificial Intelligence Machine Learning and Data Science · 2023
In artificial intelligence (AI) and machine learning (ML), transfer learning is a well-known technique that enables models to generalize knowledge from one domain to another with little data.Its ability to overcome the difficulties of limited labeled data, particularly in complex tasks where obtaining large amounts of labeled data is costly or impractical, has drawn a lot of attention.This essay examines the idea of transfer learning, its uses and different methods that make it easier to move knowledge from one field to another.We discuss the advantages and disadvantages of several important approaches, including few-shot learning, domain adaptation and fine-tuning.The study also addresses the issues that still need to be resolved in the field, such as reducing domain disparities and creating transfer learning algorithms that are more effective.Lastly, we examine transfer learning's prospects and how it might affect AI developments in different sectors.