What is the difference between transfer learning and fine-tuning?

What is the difference between transfer learning and fine-tuning?

Transfer learning involves taking a pre-trained model and applying it to a new task, while fine-tuning is adjusting that model on a specific dataset.

Can you give me an example of when to use each?

Sure! Use transfer learning when you have limited data for a new task. Fine-tuning is best when you have some data and want to improve performance.

Are there any downsides to using transfer learning?

Yes, transfer learning can lead to overfitting if the new task is too different from the original task the model was trained on.

How do I choose the right pre-trained model?

Choose a pre-trained model that is relevant to your task and has been trained on a similar dataset for better performance.