Calibration of Few-Shot Classification Tasks: Mitigating Misconfidence From Distribution Mismatch
As many meta-learning algorithms improve performance in solving few-shot classification problems for practical applications, the accurate prediction of uncertainty is considered essential.In meta-training, the algorithm powder cleanser treats all generated tasks equally and updates the model to perform well on training tasks.During the training, so