As AI agents are increasingly used in the real open world with unknowns or novelties, they need the ability to (1) recognize objects that (i) they have learned and (ii) detect items that they have not seen or learned before, and (2) learn the new items incrementally to become more and more knowledgeable and powerful. (1) is called novelty detection or out-of-distribution (OOD) detection and (2) is called class incremental learning (CIL), which is a setting of continual learning (CL). In existing research, OOD detection and CIL are regarded as two completely different problems. This paper theoretically proves that OOD detection actually is necessary for CIL. We first show that CIL can be decomposed into two sub-problems: within-task prediction (WP) and task-id prediction (TP). We then prove that TP is correlated with OOD detection. The key theoretical result is that regardless of whether WP and OOD detection (or TP) are defined explicitly or implicitly by a CIL algorithm, good WP and good OOD detection are necessary and sufficient conditions for good CIL, which unifies novelty or OOD detection and continual learning (CIL, in particular). A good CIL algorithm based on our theory can naturally be used in open world learning, which is able to perform both novelty/OOD detection and continual learning. Based on the theoretical result, new CIL methods are also designed, which outperform strong baselines in terms of CIL accuracy and its continual OOD detection by a large margin.

论文理论证明了，对于class incremental learning来说，out-of-distribution detection实际上是必需的。因此，一个好的class incremental learning算法必须要同时具备良好的within-task prediction和out-of-distribution detection能力，这将统一 novelty 或 out-of-distribution detection 以及 continual learning.

开放世界持续学习: 统一新颖性检测和持续学习