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DMAD: Dual Memory Bank for Real-World Anomaly Detection
March 20, 2024, 4:42 a.m. | Jianlong Hu, Xu Chen, Zhenye Gan, Jinlong Peng, Shengchuan Zhang, Jiangning Zhang, Yabiao Wang, Chengjie Wang, Liujuan Cao, Rongrong Ji
cs.LG updates on arXiv.org arxiv.org
Abstract: 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. To address the challenge of real-world anomaly detection, we propose a new framework named Dual Memory bank enhanced representation learning for Anomaly Detection (DMAD). This framework handles both unsupervised and …
abstract anomaly anomaly detection arxiv bank class cs.cv cs.lg data detection efficiency however industrial memory normal practical storage training type unified model world
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