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.

翻译：从神经网络中评估每个样本的不确定性量化对于涉及高风险应用的决策至关重要。一种常见方法是使用贝叶斯或近似模型的预测分布，并将相应的预测不确定性分解为认知（模型相关）和偶然（数据相关）成分。然而，加性分解近期受到了质疑。在本研究中，我们提出了一种基于不同模型预测间类别概率分布信噪比的不确定性估计与分解的直观框架。我们引入了一种方差门控度量，该度量通过从集成模型导出的置信因子对预测进行缩放。我们利用该度量讨论了委员会机器多样性崩溃的存在性问题。