CADIC: Continual Anomaly Detection Based on Incremental Coreset

• URL: http://arxiv.org/abs/2511.08634v1
• Date: Thu, 13 Nov 2025 01:01:12 GMT
• Title: CADIC: Continual Anomaly Detection Based on Incremental Coreset
• Authors: Gen Yang, Zhipeng Deng, Junfeng Man,
• Abstract summary: We propose a novel CAD framework where all tasks share a unified memory bank.<n>During training, the method incrementally updates embeddings within a fixed-size coreset.<n>In the inference phase, anomaly scores are computed via a nearest-neighbor matching mechanism.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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