论文标题
评估预测不确定性估计的鲁棒性：基于Dirichlet的模型是否可靠？
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?
论文作者
论文摘要
基于Dirichlet的不确定性（DBU）模型是最近且有希望的不确定性感知模型。 DBU模型预测了Dirichlet分布的参数，可与类预测一起提供快速，高质量的不确定性估计。在这项工作中，我们介绍了第一个大规模的，深入的研究，对在对抗攻击下DBU模型的鲁棒性。我们的结果表明，DBU模型的不确定性估计并非强大的W.R.T.三个重要任务：（1）正确和错误地分类的样本； （2）检测对抗性例子； （3）区分分布（ID）和分布（OOD）数据。此外，我们探讨了使DBU模型更强大的第一个方法。尽管对抗训练的效果很小，但我们的基于平滑的方法可显着提高DBU模型的鲁棒性。
Dirichlet-based uncertainty (DBU) models are a recent and promising class of uncertainty-aware models. DBU models predict the parameters of a Dirichlet distribution to provide fast, high-quality uncertainty estimates alongside with class predictions. In this work, we present the first large-scale, in-depth study of the robustness of DBU models under adversarial attacks. Our results suggest that uncertainty estimates of DBU models are not robust w.r.t. three important tasks: (1) indicating correctly and wrongly classified samples; (2) detecting adversarial examples; and (3) distinguishing between in-distribution (ID) and out-of-distribution (OOD) data. Additionally, we explore the first approaches to make DBU models more robust. While adversarial training has a minor effect, our median smoothing based approach significantly increases robustness of DBU models.
