Transfer Learning in Machine Learning: A Review of Methods and Applications

Abstract

Transfer learning has gained significant traction and popularity in the field of machine learning due to its wide range of potential applications. This review article offers a thorough examination of transfer learning techniques and their wide-ranging applications in several fields. This text provides a thorough evaluation of the literature, focusing on important research and the methodology used. Furthermore, a comparative table highlighting transfer learning research across several areas provides valuable insights into the wide range of applications. The inclusion criteria were centred on recent articles published within the past five years that comprehensively examined transfer learning methodologies, applications, frameworks, problems, and future directions. The review articles highlight the widespread use of transfer learning models, the effectiveness of data augmentation strategies, and the capability of transfer learning to tackle issues particular to different domains. Nevertheless, some constraints like as biases in the dataset, difficulties in interpreting the model, and problems with scalability have been recognised. These limitations provide opportunities for future research to focus on creating transfer learning algorithms that are more resilient and easier to read.