Transfer Learning Techniques in Computer Vision
Abstract
Transfer learning has gained significant prominence in the field of computer vision, allowing the reutilization of pre-trained models to improve the performance of new tasks. This paper explores various transfer learning techniques in computer vision and their applications. We discuss the process of fine-tuning, feature extraction, and domain adaptation, highlighting their advantages and limitations. The paper also presents case studies that showcase the effectiveness of these techniques in real-world scenarios, from image classification to object detection. Furthermore, we discuss future research directions and challenges in the field of transfer learning in computer vision.
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References
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