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.

该研究论文提出了一种统一的混合注意力自动编码器（MAAE），用于通过单一模型实现多类异常检测，并通过采用空间注意力和通道注意力来有效捕捉全局类别信息和模拟多个类别的特征分布，同时提出了自适应噪声生成器和多尺度融合模块以模拟特征的真实噪声并保留不同类别对象的表面语义，MAAE 在基准数据集上表现出卓越的性能。

多类工业异常检测的混合注意力自编码器

mixed attention auto encoder for multi-class industrial anomaly  detection

Previous works on unsupervised industrial anomaly detection mainly focus on
local structural anomalies such as cracks and color contamination. While
achieving significantly high detection performance on this kind of anomaly,
they are faced with logical anomalies that violate the long-range dependencies
such as a normal object placed in the wrong position. In this paper, based on
previous knowledge distillation works, we propose to use two students (local
and global) to better mimic the teacher's behavior. The local student, which is
used in previous studies mainly focuses on structural anomaly detection while
the global student pays attention to logical anomalies. To further encourage
the global student's learning to capture long-range dependencies, we design the
global context condensing block (GCCB) and propose a contextual affinity loss
for the student training and anomaly scoring. Experimental results show the
proposed method doesn't need cumbersome training techniques and achieves a new
state-of-the-art performance on the MVTec LOCO AD dataset.

基于先前知识蒸馏方法，本文提出使用两个学生（局部和全局）更好地模仿教师的行为，局部学生主要关注结构异常检测，而全局学生则关注逻辑异常，并通过设计全局上下文压缩块（GCCB）和提出上下文相似度损失来进一步鼓励全局学生学习以捕捉长距离依赖关系，实验结果表明该方法在 MVTec LOCO AD 数据集上达到了最新的性能水平，且无需繁琐的训练技巧。