Before revision: 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 protocolfor 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.
After revision: 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 / \mathcal{Q / T protocol offers a more accurate evaluation since it does not rely on possibly biased and noisy query instances.