Neural networks are powerful tools in various applications, and quantifying their uncertainty is crucial for reliable decision-making. In the deep learning field, the uncertainties are usually categorized into aleatoric (data) and epistemic (model) uncertainty. In this paper, we point out that the existing popular variance attenuation method highly overestimates aleatoric uncertainty. To address this issue, we propose a new estimation method by actively de-noising the observed data. By conducting a broad range of experiments, we demonstrate that our proposed approach provides a much closer approximation to the actual data uncertainty than the standard method.

翻译：神经网络在各种应用中都是强大的工具，量化其不确定性对于可靠决策至关重要。在深度学习领域，不确定性通常分为随机（数据）不确定性和认知（模型）不确定性。本文指出，现有的流行方差衰减方法严重高估了随机不确定性。为解决该问题，我们提出了一种通过主动对观测数据进行去噪的新估计方法。通过开展广泛的实验，我们证明了所提出的方法相比标准方法能更接近地逼近实际数据的不确定性。