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 treats all generated tasks equally and updates the model to perform well on tra...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9777704/ |