MCD在不确定性估计上不可靠，尤其在数据外推时表现差。
Unreliable Uncertainty Estimates with Monte Carlo Dropout
- 用推理时的随机丢弃模拟贝叶斯推断，生成多组预测估算不确定度。
- 实验显示其在数据外推和插值区域无法准确反映真实不确定度。
- 相比贝叶斯神经网络和高斯过程，可靠性更低，不适用于安全关键场景。
可靠的不确定性估计对机器学习模型至关重要，尤其是在安全敏感领域。尽管精确的贝叶斯推断在深层神经网络中计算成本过高，但蒙特卡洛丢弃（Monte Carlo Dropout, MCD）被提出作为深度学习中贝叶斯推断的一种高效近似方法，通过在推理阶段应用神经元丢弃生成多个子模型，从而得到预测分布以估计不确定性。本文通过实验评估其捕捉真实不确定性的能力，并与高斯过程（Gaussian Processes, GP）和贝叶斯神经网络（Bayesian Neural Networks, BNN）进行对比。结果表明，MCD在捕捉实际不确定度方面存在明显缺陷，特别是在外推和插值区域未能有效体现不确定性增长，而传统贝叶斯方法则表现出更强的鲁棒性。这说明在当前实现与评估条件下，MCD的不确定性估计不如经典贝叶斯方法可靠，难以同时准确建模认知不确定性和随机不确定性。
原文摘要 · Abstract (English)
Reliable uncertainty estimation is crucial for machine learning models, especially in safety-critical domains. While exact Bayesian inference offers a principled approach, it is often computationally infeasible for deep neural networks. Monte Carlo dropout (MCD) was proposed as an efficient approximation to Bayesian inference in deep learning by applying neuron dropout at inference time \citep{gal2016dropout}. Hence, the method generates multiple sub-models yielding a distribution of predictions to estimate uncertainty. We empirically investigate its ability to capture true uncertainty and compare to Gaussian Processes (GP) and Bayesian Neural Networks (BNN). We find that MCD struggles to accurately reflect the underlying true uncertainty, particularly failing to capture increased uncertainty in extrapolation and interpolation regions as observed in Bayesian models. The findings suggest that uncertainty estimates from MCD, as implemented and evaluated in these experiments, is not as reliable as those from traditional Bayesian approaches for capturing epistemic and aleatoric uncertainty.
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