What is transfer learning? How does it work?

Given a source domain D_S and learning task T_S, a target domain D_T and learning task T_T, transfer learning aims to help improve the learning of the target predictive function f_T in D_T using the knowledge in D_S and T_S, where D_S ≠ D_T,or T_S ≠ T_T. In other words, transfer learning enables to reuse knowledge coming from other domains or learning tasks.

In the context of CNNs, we can use networks that were pre-trained on popular datasets such as ImageNet. We then can use the weights of the layers that learn to represent features and combine them with a new set of layers that learns to map the feature representations to the given classes. Two popular strategies are either to freeze the layers that learn the feature representations completely, or to give them a smaller learning rate.

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