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Calibration of Few-Shot Classification Tasks: Mitigating Misconfidence From Distribution Mismatch

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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 treats all generated tasks equally and updates the model to perform well on training tasks. https://jalyttlers.shop/product-category/doilies/
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