Can you explain the concept of 'fine-tuning' in Transfer Learning?

Example Answer

Fine-tuning means continuing to train a pretrained model on a target task so its weights adapt to the new objective. Instead of using the pretrained model only as a fixed feature extractor, you let some or all of the network update to fit the target data.

The practical challenge is deciding how much to fine-tune. Too little adaptation may leave performance on the table, while too much can overfit a small dataset or erase useful source-task knowledge. Good fine-tuning balances adaptation with preservation.

Common Poor Answer

A weak answer says fine-tuning is retraining the model on new data, without explaining that it starts from pretrained weights and must balance adaptation carefully.