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Arxiv
2d
122
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Uncertainty Weighted Gradients for Model Calibration
- Model calibration is important for accurate predictions in deep neural networks.
- Existing loss functions, like focal loss, fail to achieve optimal calibration performance.
- A new loss framework is proposed, addressing misalignment and uncertainty estimation issues.
- Extensive experiments show the proposed method achieves state-of-the-art performance.
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