Traditional neural networks provide deterministic predictions without inherent uncertainty estimates. While Bayesian Neural Networks (BNNs) offer a principled approach to uncertainty quantification, their computational complexity limits scalability. Monte Carlo (MC) Dropout, initially introduced as a regularization technique, has been shown to approximate Bayesian inference by enabling probabilistic modeling through multiple stochastic forward passes. In this work, we enhance uncertainty estimation in deep learning by integrating a Dirichlet-based framework within MC Dropout. Specifically, we leverage the formulation proposed by Sensoy et al. (2018), where class probabilities are modeled using a Dirichlet distribution, allowing for a more informative uncertainty representation. The proposed approach maintains the computational efficiency of MC Dropout while improving the quality of uncertainty estimates. We discuss the theoretical foundations of our method and compare it with existing uncertainty quantification techniques. The results highlight the effectiveness of the proposed method in producing well-calibrated uncertainty estimates, offering a practical solution for uncertainty-aware deep learning models.

翻译：传统神经网络提供确定性预测，缺乏固有的不确定性估计。尽管贝叶斯神经网络为不确定性量化提供了原则性方法，但其计算复杂度限制了可扩展性。蒙特卡洛丢弃法最初作为正则化技术引入，已被证明可通过多次随机前向传播实现概率建模，从而近似贝叶斯推断。本文通过将基于Dirichlet的框架集成到MC Dropout中，增强深度学习的不确定性估计。具体而言，我们利用了Sensoy等人（2018）提出的公式，其中类别概率使用Dirichlet分布建模，从而提供更具信息性的不确定性表示。所提出的方法在保持MC Dropout计算效率的同时，提升了不确定性估计的质量。我们讨论了所提方法的理论基础，并将其与现有不确定性量化技术进行了比较。结果凸显了所提方法在生成良好校准的不确定性估计方面的有效性，为不确定性感知的深度学习模型提供了实用解决方案。