  The support/query (S/Q) training protocol is widely used in meta-learning.
S/Q protocol trains a task-specific model on S and then evaluates it on Q to
optimize the meta-model using query loss, which depends on size and quality of
Q. In this paper, we study a new S/T protocol for meta-learning. Assuming that
we have access to the theoretically optimal model T for a task, we can directly
match the task-specific model trained on S to T. S/T protocol offers a more
accurate evaluation since it does not rely on possibly biased and noisy query
instances. There are two challenges in putting S/T protocol into practice.
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 target models by fine-tuning
on local tasks the globally pre-trained meta-model, maintaining both efficiency
and veracity.
