  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.
