The past few years have witnessed the resurgence of uncertainty estimation generally in neural networks. Providing uncertainty quantification besides the predictive probability is desirable to reflect the degree of belief in the model’s decision about a given input. Recently, Monte-Carlo dropout (MC-dropout) method has been introduced as a probabilistic approach based Bayesian approximation which is computationally efficient than Bayesian neural networks. MC-dropout has revealed promising results on image datasets regarding uncertainty quantification. However, this method has been subjected to criticism regarding the behaviour of MC-dropout and what type of uncertainty it actually captures. For this purpose, we aim to discuss the behaviour of MC-dropout on classification tasks using synthetic and real data. We empirically explain different cases of MC-dropout that reflects the relative merits of this method. Our main finding is that MC-dropout captures datapoints lying on the decision boundary between the opposed classes using synthetic data. On the other hand, we apply MC-dropout method on dataset derived from Bitcoin known as Elliptic data to highlight the outperformance of model with MC-dropout over standard model. A conclusion and possible future directions are proposed.

过去几年见证了神经网络中不确定性估计的普遍出现。除了预测概率外，还需要提供不确定性量化，以反映对有关给定输入的模型决策的信任程度。最近，蒙特卡洛辍学（MC-dropout）方法已被引入作为一种基于概率的贝叶斯近似方法，该方法在计算上比贝叶斯神经网络有效。MC-dropout揭示了关于不确定性量化的图像数据集上令人鼓舞的结果。但是，这种方法一直受到关于MC丢失行为及其实际捕获的不确定性类型的批评。为此，我们旨在讨论使用合成和真实数据进行分类任务时MC丢失的行为。我们从经验上解释了MC丢失的不同情况，这些情况反映了此方法的相对优点。我们的主要发现是MC丢弃使用合成数据捕获了位于相对类之间的决策边界上的数据点。另一方面，我们将MC-dropout方法应用于从称为Elliptic数据的比特币衍生的数据集中，以突出MC-dropout模型优于标准模型的模型的性能。提出结论和可能的未来方向。