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

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

Transfer learning involves taking a pre-trained model and applying it to a new but related task. Fine-tuning is a specific approach where you take that pre-trained model and continue training it on your new dataset to adapt it more closely to your specific task.

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

Sure! Use transfer learning when you have a small dataset for a similar task, like using a model trained on ImageNet for a specific image classification task. Fine-tuning is ideal when you have a larger dataset and want to improve the model's performance on that specific task.

Are there any downsides to using these methods?

Yes, transfer learning can lead to suboptimal performance if the source and target tasks are too different. Fine-tuning can also overfit the model to the new dataset if it's too small.

How do I choose the right approach for my project?

Consider the size and similarity of your dataset to the pre-trained model's dataset. If your dataset is small and similar, transfer learning might suffice. If you have more data and want better performance, fine-tuning is the way to go.