多专家架构实现持续异常检测，动态分配专家并减少知识遗忘。
MECAD: A multi-expert architecture for continual anomaly detection
- 基于特征相似性动态分配专家，实现类别自适应
- 5专家配置平均AUROC达0.8259，显著降低知识退化
- 适合产品类型频繁更新的工业场景
本文提出MECAD，一种基于多专家架构的持续异常检测方法。系统根据特征相似性动态分配专家，并通过优化的核集选择与专用重放缓冲机制，实现增量学习而无需全模型重训练。在MVTec AD数据集上的实验表明，最优5专家配置在15个不同物体类别上平均AUROC达到0.8259，相比单专家方法显著减少知识退化。该框架兼顾计算效率、专长知识保留与适应性，适用于产品类型不断变化的工业环境。
原文摘要 · Abstract (English)
In this paper we propose MECAD, a novel approach for continual anomaly detection using a multi-expert architecture. Our system dynamically assigns experts to object classes based on feature similarity and employs efficient memory management to preserve the knowledge of previously seen classes. By leveraging an optimized coreset selection and a specialized replay buffer mechanism, we enable incremental learning without requiring full model retraining. Our experimental evaluation on the MVTec AD dataset demonstrates that the optimal 5-expert configuration achieves an average AUROC of 0.8259 across 15 diverse object categories while significantly reducing knowledge degradation compared to single-expert approaches. This framework balances computational efficiency, specialized knowledge retention, and adaptability, making it well-suited for industrial environments with evolving product types.
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