Soft-Label Supervised Meta-Model with Adversarial Samples for Uncertainty Quantification
Despite the recent success of deep-learning models, traditional models are overconfident and poorly calibrated. This poses a serious problem when applied to high-stakes applications. To solve this issue, uncertainty quantification (UQ) models have been developed to allow the detection of misclassifi...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/14/1/12 |
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