Unsupervised Continual Learning: A Review of Challenges, Techniques, and Evaluation Metrics

Unsupervised continual learning (UCL) aims to develop learning systems that can acquire knowledge from a sequence of unlabeled and potentially non-stationary data while retaining previously learned information, without relying on task labels or explicit supervision. By combining unsupervised learning with continual learning (CL), UCL provides a framework for autonomous adaptation in dynamic environments. This review presents a comprehensive synthesis of the state of the art in UCL, covering fundamental principles, representative methods, key challenges, and evaluation practices. Despite recent progress, major obstacles remain, including catastrophic forgetting, distribution shift in streaming data, and scalability in memory and computation.We summarize these challenges and organize mainstream solutions accordingly, and we review commonly used benchmarks and metrics to clarify how UCL methods are evaluated and compared. Finally, we identify key gaps and suggest directions for future UCL research.