What is the difference between transfer learning and training from scratch?

What is the difference between transfer learning and training from scratch?

Transfer learning involves using a pre-trained model on a new task, while training from scratch means building a model from the ground up.

When should I use transfer learning?

You should use transfer learning when you have limited data for your task, as it can leverage knowledge from related tasks.

Are there any downsides to transfer learning?

Yes, transfer learning can lead to negative transfer if the pre-trained model is not well-suited for the new task.

Can you give an example of transfer learning?

Sure! A common example is using a model trained on ImageNet for a specific image classification task, like identifying medical images.