  Although continual learning and anomaly detection have separately been
well-studied in previous works, their intersection remains rather unexplored.
The present work addresses a learning scenario where a model has to
incrementally learn a sequence of anomaly detection tasks, i.e. tasks from
which only examples from the normal (majority) class are available for
training. We define this novel learning problem of continual anomaly detection
(CAD) and formulate it as a meta-learning problem. Moreover, we propose A Rapid
Continual Anomaly Detector (ARCADe), an approach to train neural networks to be
robust against the major challenges of this new learning problem, namely
catastrophic forgetting and overfitting to the majority class. The results of
our experiments on three datasets show that, in the CAD problem setting, ARCADe
substantially outperforms baselines from the continual learning and anomaly
detection literature. Finally, we provide deeper insights into the learning
strategy yielded by the proposed meta-learning algorithm.
