Before revision:  Firstly, we have to determine how to match the task-specific model to T. To this end, we minimize the discrepancy between them on a fictitious dataset generated by adversarial learning, and distill the prediction ability of T to the task-specific model. Secondly, we usually do not have ready-made optimal models. As an alternative, we construct surrogate/\mathcal{T} protocol in a typical application of meta-learning, \mathit{i.e.}, few-shot learning.
After revision:  We find that with a small ratio of tasks armed with target models, classic meta-learning algorithms can be improved a lot without consuming many resources. Furthermore, we empirically verify the effectiveness of \mathcal{S/\mathcal{T} protocol in a typical application of meta-learning, \mathit{i.e.}, few-shot learning.