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IEEE/ICACT20220392 Slide.06        [Big Slide]	Oral Presentation
	Meta-training and Meta-testing Meta-training N classes are extracted from the training data Mtrain. For each of these n classes, two subset examples are sampled as the support set MSupport and the query set Mquery. Next, input the support set MSupport and the query set Mquery into the model, and update the parameters by minimizing the loss in the query set Mquery. The above process is called Meta training and will be repeated many times. Meta-testing After Meta training, the performance of the model will be evaluated through the same event based mechanism. In one test, n new classes will be extracted from Mtest, which does not intersect Mtrain. Then, the support set and test set are extracted from N classes. Model parameters can be fine-tuned through small support sets.

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