Continual Evidential Deep Learning for Out-of-Distribution Detection
This work addresses the challenge of reliable uncertainty estimation in continual learning for AI systems, though it is incremental as it combines existing methods.
The paper tackles the problem of performing incremental object classification and out-of-distribution (OOD) detection simultaneously by integrating evidential deep learning into a continual learning framework, achieving comparable classification results and largely outperforming baseline methods in OOD detection on metrics like AUROC, AUPR, and FPR95.
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.