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.
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 with the evaluation loss of a series of task-specific solvers. Most existing algorithms sample non-overlapping \mathit{support / T protocolfor meta-learning.