DMAD: Dual Memory Bank for Real-World Anomaly Detection
收藏DataCite Commons2024-12-17 更新2025-04-16 收录
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https://service.tib.eu/ldmservice/dataset/7bc46586-2dd9-467e-98d6-f872b660dcb5
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资源简介：
Training a unified model is considered to be more suitable for practical industrial anomaly detection scenarios due to its generalization ability and storage efficiency. However, this multi-class setting, which exclusively uses normal data, overlooks the few but important accessible annotated anomalies in the real world.
提供机构：
TIB
创建时间：
2024-12-17
