A Survey of Transfer Learning in Computer Vision
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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References
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