Conclusion

Throughout this chapter, we explored transfer learning, a powerful technique that leverages models pre-trained on large datasets to solve new tasks efficiently. By reusing learned representations, transfer learning helps achieve high performance even with limited data, reducing both computational costs and training time.

A common challenge in deep learning is working with very limited data, ranging from a few hundreds to tens of thousands of images. It is often said that deep learning requires large amounts of data to be effective. This is partly true, because training a convolutional network (CNN) from scratch on a small dataset is often impractical due to overfitting.

Fortunately, transfer learning provides a way around this limitation. A pre-trained model can generalize well even with relatively little data, especially when the task is simple and the model is well-regularized. This is effective because the features learned by the convolutional backbone of a CNN are often general, and can be used to solve a wide range of problems, not just the ones they were trained on.