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MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection
April 11, 2024, 4:44 a.m. | Haoyang He, Yuhu Bai, Jiangning Zhang, Qingdong He, Hongxu Chen, Zhenye Gan, Chengjie Wang, Xiangtai Li, Guanzhong Tian, Lei Xie
cs.CV updates on arXiv.org arxiv.org
Abstract: Recent advancements in anomaly detection have seen the efficacy of CNN- and transformer-based approaches. However, CNNs struggle with long-range dependencies, while transformers are burdened by quadratic computational complexity. Mamba-based models, with their superior long-range modeling and linear efficiency, have garnered substantial attention. This study pioneers the application of Mamba to multi-class unsupervised anomaly detection, presenting MambaAD, which consists of a pre-trained encoder and a Mamba decoder featuring Locality-Enhanced State Space (LSS) modules at multi-scales. The …
abstract anomaly anomaly detection arxiv attention class cnn cnns complexity computational cs.cv dependencies detection efficiency however linear mamba modeling space state state space models struggle study transformer transformers type unsupervised
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