DMAD: Dual Memory Bank for Real-World Anomaly Detection

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.

Data and Resources

Cite this as

Jianlong Hu, Xu Chen, Zhenye Gan, Jinlong Peng, Shengchuan Zhang, Jiangning Zhang, Yabiao Wang, Chengjie Wang, Liujuan Cao, Rongrong Ji (2024). Dataset: DMAD: Dual Memory Bank for Real-World Anomaly Detection. https://doi.org/10.57702/vtvtlkro

DOI retrieved: December 17, 2024

Additional Info

Field Value
Created December 17, 2024
Last update December 17, 2024
Defined In https://doi.org/10.48550/arXiv.2403.12362
Author Jianlong Hu
More Authors
Xu Chen
Zhenye Gan
Jinlong Peng
Shengchuan Zhang
Jiangning Zhang
Yabiao Wang
Chengjie Wang
Liujuan Cao
Rongrong Ji