mixed attention auto encoder for multi-class industrial anomaly
  detection
Jiangqi Liu Feng Wang
Most existing methods for unsupervised industrial anomaly detection train a
separate model for each object category. This kind of approach can easily
capture the category-specific feature distributions, but results in high
storage cost and low training efficiency. In this paper, we propose a unified
mixed-attention auto encoder (MAAE) to implement multi-class anomaly detection
with a single model. To alleviate the performance degradation due to the
diverse distribution patterns of different categories, we employ spatial
attentions and channel attentions to effectively capture the global category
information and model the feature distributions of multiple classes.
Furthermore, to simulate the realistic noises on features and preserve the
surface semantics of objects from different categories which are essential for
detecting the subtle anomalies, we propose an adaptive noise generator and a
multi-scale fusion module for the pre-trained features. MAAE delivers
remarkable performances on the benchmark dataset compared with the
state-of-the-art methods.