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Uncertainty quantification metrics for deep regression
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DOI：10.1016/j.patrec.2024.09.011.png)
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摘要
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When deploying deep neural networks on robots or other physical systems, the learned model should reliably quantify predictive uncertainty. A reliable uncertainty allows downstream modules to reason about the safety of its actions. In this work, we address metrics for uncertainty quantification. Specifically, we focus on regression tasks, and investigate Area Under Sparsification Error (AUSE), Calibration Error (CE), Spearman's Rank Correlation, and Negative Log-Likelihood (NLL). Using multiple datasets, we look into how those metrics behave under four typical types of uncertainty, their stability regarding the size of the test set, and reveal their strengths and weaknesses. Our results indicate that Calibration Error is the most stable and interpretable metric, but AUSE and NLL also have their respective use cases. We discourage the usage of Spearman's Rank Correlation for evaluating uncertainties and recommend replacing it with AUSE.
Keyword:
Uncertainty
Evaluation
Metrics
Regression
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IF:
3.3
论文数:
8.0K
被引数:
1.6W
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分享收藏Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles神经网络潜力中的单模型不确定性量化并不总是优于模型集合OAAI
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