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Architecture for meta-learning: each task mimics the few-shot scenario and can be completely non-overlapping. Support sets are used to train; query sets are used to evaluate the model. In this example, several text classification tasks on different datasets (and label sets) are used to train the meta-learner. Finally, the test task (medical domain) is used for generalizing the meta-learner to the test task.
