Deep Learning Quick Reference

Common problem domains

If your target domain is at least somewhat similar to the source domain, transfer learning tends to work well. For example, imagine you were classifying an image as containing either a cat or a dog. There are many ImageNet trained image classifiers that would be ideal to use for this type or problem.

Instead, let's imagine that our problem is to classify a CT scan or MRI as containing a tumor or not. This target domain is very different from the ImageNet source domain. As such, while there might be (and probably will be) a benefit in using transfer learning, we will need much more data and probably some fine-tuning to adapt the network to this target domain.