When to Use Transfer Learning

You should use transfer learning when you want to improve a model's performance on a new task by using knowledge from a related task. This approach is most helpful when you have limited labeled data in your target domain. If collecting or labeling data is difficult or expensive, transfer learning can save you time and resources.

Transfer learning works best when the source and target domains are similar. For example, if both tasks involve image or text data, the patterns learned from one can help with the other. Using a model trained on a large dataset as a starting point often gives better results than training from scratch, especially if your own dataset is small.

Transfer learning is also useful when training a model from scratch would take too much time or computing power. If you have limited resources, starting with a pre-trained model can make your workflow much more efficient.

Here are some real-world examples:

• In medical imaging, you might have only a small set of labeled X-rays. You can use a model trained on a large collection of natural images to improve your results, even with less data.
• For language tasks, you can take a model trained on a large text source like Wikipedia and adapt it to classify tweets, even if you have only a few labeled examples.

However, transfer learning is not always the right choice. If the source and target domains are very different—such as using image data to help with audio tasks—it can actually reduce performance. This is called negative transfer. Also, if your target task is simple and you have plenty of data, it may be easier and more effective to train a model from scratch.