Neural networks are often overconfident about their predictions, which undermines their reliability and trustworthiness. In this work, we present a novel technique, named Error-Driven Uncertainty Aware Training (EUAT), which aims to enhance the ability of neural models to estimate their uncertainty correctly, namely to be highly uncertain when they output inaccurate predictions and low uncertain when their output is accurate. The EUAT approach operates during the model's training phase by selectively employing two loss functions depending on whether the training examples are correctly or incorrectly predicted by the model. This allows for pursuing the twofold goal of i) minimizing model uncertainty for correctly predicted inputs and ii) maximizing uncertainty for mispredicted inputs, while preserving the model's misprediction rate. We evaluate EUAT using diverse neural models and datasets in the image recognition domains considering both non-adversarial and adversarial settings. The results show that EUAT outperforms existing approaches for uncertainty estimation (including other uncertainty-aware training techniques, calibration, ensembles, and DEUP) by providing uncertainty estimates that not only have higher quality when evaluated via statistical metrics (e.g., correlation with residuals) but also when employed to build binary classifiers that decide whether the model's output can be trusted or not and under distributional data shifts.

翻译：神经网络通常对其预测结果过度自信，这削弱了其可靠性与可信度。本文提出一种名为"错误驱动的感知不确定性训练"（EUAT）的新技术，旨在增强神经模型正确估计不确定性的能力，即当模型输出不准确预测时表现出高不确定性，而输出准确预测时表现出低不确定性。EUAT方法在模型训练阶段运行，根据模型对训练样本预测正确与否，选择性采用两种损失函数。此举旨在实现双重目标：一是对正确预测的输入最小化模型不确定性，二是对错误预测的输入最大化不确定性，同时保持模型的误预测率。我们采用图像识别领域中的多种神经模型与数据集，在非对抗与对抗场景下对EUAT进行评估。结果表明，EUAT在不确定性估计方面优于现有方法（包括其他感知不确定性训练技术、校准、集成及DEUP），不仅通过统计指标（如与残差的相关性）评估时质量更高，而且在构建用于判断模型输出是否可信的二元分类器以及应对数据分布漂移时表现更优。