How does transfer learning improve accuracy?

What is transfer learning and how does it work?

Transfer learning is a technique where a model developed for a specific task is reused as the starting point for a model on a second task. It leverages knowledge gained from one task to improve performance on another.

How does it actually improve accuracy?

By using pre-trained models, transfer learning allows the new model to start with weights that already capture useful features, reducing the amount of data and training time needed to achieve high accuracy.

Can you give me an example of transfer learning in action?

Sure! A common example is using a model trained on ImageNet for image classification tasks. Instead of training from scratch, you can fine-tune it on a smaller dataset, which often leads to better accuracy.

Are there any limitations to transfer learning?

Yes, transfer learning may not work well if the source and target tasks are too different, or if the pre-trained model is not suitable for the new task.