A Survey of Transfer Learning in Computer Vision

Authors

  • Prof. Jonathan Lee Author

Abstract

Transfer learning has become a fundamental technique for leveraging pre-trained models and domain knowledge to improve the performance of computer vision tasks with limited labeled data. This review paper provides a survey of transfer learning methods in computer vision, including fine-tuning, feature extraction, and domain adaptation. It explores the applications, challenges, and best practices for transferring knowledge across visual domains.

 

 

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Published

2024-05-03

Issue

Section

Articles

How to Cite

A Survey of Transfer Learning in Computer Vision. (2024). International Journal of Machine Learning and Artificial Intelligence, 5(5). https://jmlai.in/index.php/ijmlai/article/view/39