
MambaAD, a novel framework that leverages the Mamba architecture, achieves state-of-the-art performance in multi-class unsupervised anomaly detection tasks while maintaining low model complexity.


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MambaAD: A Mamba-based Approach for Efficient and Effective Multi-class Unsupervised Anomaly Detection


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This work proposes a large-scale and general-purpose COCO-AD benchmark for comprehensive evaluation of multi-class anomaly detection methods, and introduces a novel feature inversion framework (InvAD) that achieves state-of-the-art performance on this challenging benchmark as well as other popular datasets.


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Comprehensive Multi-class Anomaly Detection under a General-purpose COCO-AD Benchmark
