Wave-MambaAD: Wavelet-Driven State Space Model for Multi-Class Unsupervised Anomaly Detection

Abstract

The Mamba model excels in anomaly detection through efficient long-range dependency modeling and linear complexity. However, Mamba-based anomaly detectors still face two critical challenges: (1) insufficient modeling of diverse local features leading to inaccurate detection of subtle anomalies; (2) spatial-wise scanning mechanism disrupting the spatial continuity of large-scale anomalies, resulting in incomplete localization. To address these challenges, we propose Wave-MambaAD, a wavelet-driven state space model for unified subtle and large-scale anomaly detection. Firstly, to capture subtle anomalies, we design a high-frequency state space model that employs horizontal, vertical, and diagonal scanning mechanisms for processing directionally aligned high-frequency components, enabling precise anomaly detection through multidimensional feature extraction. Secondly, for comprehensive localization of large-scale anomalies, we propose a low-frequency state space model implementing channel-adaptive dynamic scanning mechanisms to maintain structural coherence in global contexts, which facilitates large-scale anomaly detection via adaptive feature integration. Finally, we develop a dynamic spatial enhancement block to improve anomalous feature representation by enhancing feature diversity through coordinated inter-channel communication and adaptive gating mechanisms. Comprehensive experiments on benchmark anomaly detection datasets show that Wave-MambaAD achieves competitive performance at lower parameters and computational costs.