用于多类工业异常检测的混合注意力自动编码器
mixed attention auto encoder for multi-class industrial anomaly detection
AI总结：
本文提出统一混合注意力自动编码器MAAE，以单一模型实现多类工业异常检测，通过空间与通道注意力、自适应噪声生成和多尺度融合缓解类别差异并保留表面语义，在基准数据集上取得显著性能。
摘要：
现有的大多数无监督工业异常检测方法为每个物体类别单独训练一个模型。这类方法容易捕捉类别特定的特征分布，但导致存储成本高且训练效率低。本文提出一种统一的混合注意力自动编码器（MAAE），以单一模型实现多类异常检测。为缓解不同类别多样化分布模式带来的性能下降，我们采用空间注意力和通道注意力来有效捕捉全局类别信息，并对多个类别的特征分布进行建模。此外，为模拟特征上的真实噪声，并保留来自不同类别的物体表面语义（这对检测细微异常至关重要），我们为预训练特征提出了自适应噪声生成器和多尺度融合模块。在基准数据集上，与最先进方法相比，MAAE 取得了显著的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
