Explain how transfer learning works in computer vision. What are the common strategies for fine-tuning pre-trained models, and how do you decide which layers to freeze?

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Transfer learning leverages knowledge learned from one task to improve performance on a related task. In computer vision, this typically means using models pre-trained on large datasets like ImageNet and adapting them for specific applications.