MECAD：一种多专家架构用于持续异常检测
MECAD: A multi-expert architecture for continual anomaly detection
AI总结：
MECAD通过多专家架构实现持续异常检测，利用优化的coreset选择和重放缓冲机制，有效减少知识退化，提升计算效率和适应性。
摘要：
在本文中，我们提出MECAD，一种利用多专家架构进行持续异常检测的新方法。我们的系统根据特征相似性动态地将专家分配给物体类别，并采用高效的内存管理来保留已见过类别的知识。通过利用优化的coreset选择和专门的重放缓冲机制，我们实现了无需完整模型重训练的增量学习。在MVTec AD数据集上的实验评估表明，最优的5专家配置在15种不同的物体类别上实现了平均AUROC为0.8259，同时相比单专家方法显著减少了知识退化。该框架在计算效率、专门的知识保留和适应性之间取得了平衡，使其非常适合具有演变产品类型的工业环境。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
