arXiv cs.LG
· Papers
Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios
arXiv:2607.18289v1 Announce Type: new Abstract: Continual anomaly detection (CAD) studies how models can adapt to evolving data distributions while retaining performance on previously observed regimes. CAD benchmarks, however, depend critically on how tasks are defined, filtered, ordered, and validated. In tabular doma