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Most existing methods for unsupervised industrial anomaly detection train aseparate model for each object category. This kind of approach can easilycapture the category-specific feature distributions, but results in highstorage cost and low training efficiency. In this paper, we propose a unifiedmixed-attention auto encoder (MAAE) to implement multi-class anomaly detectionwith a single model. To alleviate the performance degradation due to thediverse distribution patterns of different categories, we employ spatialattentions and channel attentions to effectively capture the global categoryinformation and model the feature distributions of multiple classes.Furthermore, to simulate the realistic noises on features and preserve thesurface semantics of objects from different categories which are essential fordetecting the subtle anomalies, we propose an adaptive noise generator and amulti-scale fusion module for the pre-trained features. MAAE deliversremarkable performances on the benchmark dataset compared with thestate-of-the-art methods.

mixed attention auto encoder for multi-class industrial anomaly detection