CADIC: Continual Anomaly Detection Based on Incremental Coreset
Continual anomaly detection (CAD) faces dual challenges: learning normal patterns for new tasks under dynamic data distributions while mitigating catastrophic forgetting. Existing embedding-based approaches rely on task-specific sub-memory banks, leading to fragmented knowledge and poor scalability. This paper proposes a unified memory bank mechanism, employing a shared, fixed-size incremental coreset to enable cross-task knowledge accumulation and efficient memory updates; anomaly scores are computed via embedding learning combined with nearest-neighbor matching. On MVTec AD and Visa datasets, the method achieves image-level AUROC scores of 0.972 and 0.891, respectively, and attains 100% anomaly detection rate on the E-Paper dataset—substantially outperforming state-of-the-art methods. The core contribution lies in decoupling task boundaries, thereby enhancing model flexibility and scalability without requiring explicit task identifiers or architectural expansion.