Open-World Continual Learning: Unifying Novelty Detection and Continual Learning

• URL: http://arxiv.org/abs/2304.10038v2
• Date: Mon, 21 Oct 2024 04:05:25 GMT
• Title: Open-World Continual Learning: Unifying Novelty Detection and Continual Learning
• Authors: Gyuhak Kim, Changnan Xiao, Tatsuya Konishi, Zixuan Ke, Bing Liu,
• Abstract summary: We show that good OOD detection for each task within the set of learned tasks is necessary for successful CIL. We then prove that the theory can be generalized or extended to open-world CIL, which can perform CIL in the open world and detect future or open-world OOD data. New CIL methods are also designed, which outperform strong baselines in CIL accuracy and in continual OOD detection by a large margin.
• Score: 20.789113765332935
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As AI agents are increasingly used in the real open world with unknowns or novelties, they need the ability to (1) recognize objects that (a) they have learned before and (b) detect items that they have never seen or learned, 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 first provides a theoretical proof that good OOD detection for each task within the set of learned tasks (called closed-world OOD detection) is necessary for successful CIL. We show this by decomposing CIL into two sub-problems: within-task prediction (WP) and task-id prediction (TP), and proving that TP is correlated with closed-world 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 closed-world OOD detection are necessary and sufficient conditions for good CIL, which unifies novelty or OOD detection and continual learning (CIL, in particular). We call this traditional CIL the closed-world CIL as it does not detect future OOD data in the open world. The paper then proves that the theory can be generalized or extended to open-world CIL, which is the proposed open-world continual learning, that can perform CIL in the open world and detect future or open-world OOD data. Based on the theoretical results, new CIL methods are also designed, which outperform strong baselines in CIL accuracy and in continual OOD detection by a large margin.

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