CADIC: Continual Anomaly Detection Based on Incremental Coreset

TL;DR

This paper introduces CADIC, a continual anomaly detection framework using a shared memory bank and incremental coreset updates, achieving state-of-the-art accuracy without task-specific memory fragmentation.

Contribution

It proposes a novel unified memory bank approach with incremental coreset updates for continual anomaly detection, improving scalability and performance.

Findings

Achieves state-of-the-art AUROC scores of 0.972 on MVTec AD

Attains 100% anomaly detection accuracy on a real-world dataset

Outperforms existing methods in continual anomaly detection scenarios

Abstract

The primary objective of Continual Anomaly Detection (CAD) is to learn the normal patterns of new tasks under dynamic data distribution assumptions while mitigating catastrophic forgetting. Existing embedding-based CAD approaches continuously update a memory bank with new embeddings to adapt to sequential tasks. However, these methods require constructing class-specific sub-memory banks for each task, which restricts their flexibility and scalability. To address this limitation, we propose a novel CAD framework where all tasks share a unified memory bank. During training, the method incrementally updates embeddings within a fixed-size coreset, enabling continuous knowledge acquisition from sequential tasks without task-specific memory fragmentation. In the inference phase, anomaly scores are computed via a nearest-neighbor matching mechanism, achieving state-of-the-art detection accuracy. We validate the method through comprehensive experiments on MVTec AD and Visa datasets. Results show that our approach outperforms existing baselines, achieving average image-level AUROC scores of 0.972 (MVTec AD) and 0.891 (Visa). Notably, on a real-world electronic paper dataset, it demonstrates 100% accuracy in anomaly sample detection, confirming its robustness in practical scenarios. The implementation will be open-sourced on GitHub.