  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 proposed LSS
module, integrating parallel cascaded (Hybrid State Space) HSS blocks and
multi-kernel convolutions operations, effectively captures both long-range and
local information. The HSS block, utilizing (Hybrid Scanning) HS encoders,
encodes feature maps into five scanning methods and eight directions, thereby
strengthening global connections through the (State Space Model) SSM. The use
of Hilbert scanning and eight directions significantly improves feature
sequence modeling. Comprehensive experiments on six diverse anomaly detection
datasets and seven metrics demonstrate state-of-the-art performance,
substantiating the method's effectiveness.
