对比两种不确定性估计方法，提升模型可靠性。
Beyond Accuracy: Reliability and Uncertainty Estimation in Convolutional Neural Networks
- 用蒙特卡洛丢弃和置信区间预测评估不确定性。
- GoogLeNet比VGG16更可靠，预测集具统计保证。
- 适合高风险决策场景的可信AI系统构建。
深度神经网络（DNN）因灵活且预测性能强，被广泛应用于各类科学与实际场景。然而，尽管精度高，其校准性差，常对错误预测赋予过高置信度。本文比较了两种不确定性量化方法：基于蒙特卡洛丢弃的贝叶斯近似与非参数置信区间预测框架。在Fashion-MNIST数据集上，使用H-CNN VGG16和GoogLeNet两种卷积神经网络进行评估。结果表明，虽然H-CNN VGG16预测准确率更高，但存在明显过自信现象；而GoogLeNet的不确定性估计更具校准性。置信区间预测还展现出一致的有效性，生成具有统计保障的预测集，在高风险决策中具实用价值。研究强调应超越准确率评价模型性能，推动更可靠、可信赖的深度学习系统发展。
原文摘要 · Abstract (English)
Deep neural networks (DNNs) have become integral to a wide range of scientific and practical applications due to their flexibility and strong predictive performance. Despite their accuracy, however, DNNs frequently exhibit poor calibration, often assigning overly confident probabilities to incorrect predictions. This limitation underscores the growing need for integrated mechanisms that provide reliable uncertainty estimation. In this article, we compare two prominent approaches for uncertainty quantification: a Bayesian approximation via Monte Carlo Dropout and the nonparametric Conformal Prediction framework. Both methods are assessed using two convolutional neural network architectures; H-CNN VGG16 and GoogLeNet, trained on the Fashion-MNIST dataset. The empirical results show that although H-CNN VGG16 attains higher predictive accuracy, it tends to exhibit pronounced overconfidence, whereas GoogLeNet yields better-calibrated uncertainty estimates. Conformal Prediction additionally demonstrates consistent validity by producing statistically guaranteed prediction sets, highlighting its practical value in high-stakes decision-making contexts. Overall, the findings emphasize the importance of evaluating model performance beyond accuracy alone and contribute to the development of more reliable and trustworthy deep learning systems.
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