用信号噪声比思想重构不确定性估计，解决模型分歧与置信度问题。
Uncertainty Estimation using Variance-Gated Distributions
- 基于预测分布的信噪比设计新度量，替代传统分解方法
- 发现集成模型在高不确定性时出现预测多样性下降现象
- 适合需要可靠置信度评估的医疗、自动驾驶等高风险场景
神经网络的每样本不确定性量化对高风险决策至关重要。现有方法通常基于贝叶斯或近似模型的预测分布，将不确定性分解为认知型（模型相关）和随机型（数据相关）两部分。但这种加性分解近年受到质疑。本文提出一种直观的不确定性估计与分解框架，基于不同模型预测下类别概率分布的信噪比。引入方差门控度量，通过集成学习得到的置信度因子缩放预测结果。利用该度量探讨了委员会机器预测多样性的坍缩现象。
原文摘要 · Abstract (English)
Evaluation of per-sample uncertainty quantification from neural networks is essential for decision-making involving high-risk applications. A common approach is to use the predictive distribution from Bayesian or approximation models and decompose the corresponding predictive uncertainty into epistemic (model-related) and aleatoric (data-related) components. However, additive decomposition has recently been questioned. In this work, we propose an intuitive framework for uncertainty estimation and decomposition based on the signal-to-noise ratio of class probability distributions across different model predictions. We introduce a variance-gated measure that scales predictions by a confidence factor derived from ensembles. We use this measure to discuss the existence of a collapse in the diversity of committee machines.
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