Uncertainty-based deep learning models have attracted a great deal of interest for their ability to provide accurate and reliable predictions. Evidential deep learning stands out achieving remarkable performance in detecting out-of-distribution (OOD) data with a single deterministic neural network. Motivated by this fact, in this paper we propose the integration of an evidential deep learning method into a continual learning framework in order to perform simultaneously incremental object classification and OOD detection. Moreover, we analyze the ability of vacuity and dissonance to differentiate between in-distribution data belonging to old classes and OOD data. The proposed method, called CEDL, is evaluated on CIFAR-100 considering two settings consisting of 5 and 10 tasks, respectively. From the obtained results, we could appreciate that the proposed method, in addition to provide comparable results in object classification with respect to the baseline, largely outperforms OOD detection compared to several posthoc methods on three evaluation metrics: AUROC, AUPR and FPR95.

翻译：基于不确定性的深度学习模型因能够提供准确且可靠的预测而备受关注。其中，证据深度学习凭借单一确定性神经网络在检测分布外（OOD）数据方面展现出了卓越性能。受此启发，本文提出将证据深度学习方法集成到持续学习框架中，以同步实现增量式目标分类与OOD检测。此外，我们分析了空虚度与不调和度在区分属于旧类别的分布内数据与OOD数据方面的能力。所提出的方法被称为CEDL，我们在CIFAR-100数据集上分别基于5个任务和10个任务的两种设置进行了评估。从实验结果可以看出，所提方法不仅在目标分类方面与基线方法取得了可比的结果，而且在AUROC、AUPR和FPR95三项评估指标上，相较于多种后处理方法，在OOD检测中显著更优。