N3C: Towards Replay-based Novelty Continual Clustering with Class-Overlapping

Deep clustering has excelled in batch settings, but little work has addressed the more practical and challenging continual clustering (CC) with shifting data distributions. Additionally, class-overlapping, also a challenging issue, where classes recur across tasks, is common in real-world scenarios. In this paper, we introduce a new framework for CC with class-overlapping, integrating OOD detection to distinguish between old and new classes and a two-step deep clustering process: contrastive learning for feature representation and rehearsal-based learning to retain previous knowledge. We also propose a memory-updating strategy for handling unsupervised data. Experiments validate our approach, examining factors like OOD detection, class-overlapping levels, etc. This work advances continual clustering toward real-world applications.