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.

翻译：大多数现有的无监督工业异常检测方法针对每个物体类别分别训练一个模型。这种方法虽然能够轻易捕获类别特定的特征分布，但会导致高存储成本和低训练效率。本文提出了一种统一的混合注意力自编码器（MAAE），通过单一模型实现多类别异常检测。为了缓解不同类别分布模式差异造成的性能下降，我们采用空间注意力和通道注意力有效捕获全局类别信息，并对多类别的特征分布进行建模。此外，为模拟特征上的真实噪声并保留不同类别物体表面的语义信息（这对于检测细微异常至关重要），我们针对预训练特征提出了自适应噪声生成器和多尺度融合模块。与现有最先进方法相比，MAAE在基准数据集上取得了显著性能。