Hands-On Transfer Learning with Python

Domain confusion

We learned different transfer learning strategies and even discussed the three questions of what, when, and how to transfer knowledge from the source to the target. In particular, we discussed how feature-representation transfer can be useful. It is worth reiterating that different layers in a deep learning network capture different sets of features. We can utilize this fact to learn domain-invariant features and improve their transferability across domains. Instead of allowing the model to learn any representation, we nudge the representations of both domains to be as similar as possible.

This can be achieved by applying certain preprocessing steps directly to the representations themselves. Some of these have been discussed ...