Programming PyTorch for Deep Learning

Chapter 4. Transfer Learning and Other Tricks

Having looked over the architectures in the previous chapter, you might wonder whether you could download an already trained model and train it even further. And the answer is yes! It’s an incredibly powerful technique in deep learning circles called transfer learning, whereby a network trained for one task (e.g., ImageNet) is adapted to another (fish versus cats).

Why would you do this? It turns out that an architecture trained on ImageNet already knows an awful lot about images, and in particular, quite a bit about whether something is a cat or a fish (or a dog or a whale). Because you’re no longer starting from an essentially blank neural network, with transfer learning you’re likely to spend much less time in training, and you can get away with a vastly smaller training dataset. Traditional deep learning approaches take huge amounts of data to generate good results. With transfer learning, you can build human-level classifiers with a few hundred images.

Transfer Learning with ResNet

Now, the obvious thing to do is to create a ResNet model as we did in Chapter 3 and just slot it into our existing training loop. And you can do that! There’s nothing magical in the ResNet model; it’s built up from the same building blocks that you’ve already seen. However, it’s a big model, and although you will see some improvement over a baseline ResNet with your data, you will need a lot of data to make sure that the training signal gets to all ...