Abstract Anomaly detection provides an effective decision support capability in several real-world domains. One limitation of conventional approaches is their inability to preserve knowledge as models are constantly updated with recent data, leading to catastrophic forgetting. Continual learning approaches overcome this limitation by providing strategies that provide a trade-off between model stability and plasticity. However, to deal with concept-agnostic scenarios, transitions between tasks/concepts must be dete…