Train or Adapt a Deeply Learned Profile?

In recent years, many papers have shown that deep learning can be beneficial for profiled side-channel analysis. However, to obtain good performance with deep learning, an evaluator or an attacker face the issue of data. Due to the context, he might be limited in the amount of data for training. This can be mitigated with classical Machine Learning (ML) techniques such as data augmentation. However, these mitigation techniques lead to a significant increase in the training time; first, by augmenting the data and second, by increasing the time to perform the learning of the neural network.