Industrial image anomaly detection under the setting of one-class
classification has significant practical value. However, most existing models
struggle to extract separable feature representations when performing feature
embedding and struggle to build compact descriptions of normal features when
performing one-class classification. One direct consequence of this is that
most models perform poorly in detecting logical anomalies which violate
contextual relationships. Focusing on more effective and comprehensive anomaly
detection, we propose a network based on self-supervised learning and
self-attentive graph convolution (SLSG) for anomaly detection. SLSG uses a
generative pre-training network to assist the encoder in learning the embedding
of normal patterns and the reasoning of position relationships. Subsequently,
SLSG introduces the pseudo-prior knowledge of anomaly through simulated
abnormal samples. By comparing the simulated anomalies, SLSG can better
summarize the normal features and narrow down the hypersphere used for
one-class classification. In addition, with the construction of a more general
graph structure, SLSG comprehensively models the dense and sparse relationships
among elements in the image, which further strengthens the detection of logical
anomalies. Extensive experiments on benchmark datasets show that SLSG achieves
superior anomaly detection performance, demonstrating the effectiveness of our
method.

为了更有效地检测异常，本文提出了一种基于自监督学习和自注意力图卷积（SLSG）的网络，SLSG 使用生成式预训练网络辅助编码器学习正常模式的嵌入和位置关系的推理，并通过比较模拟异常来更好地总结正常特征。此外，SLSG 通过构建更一般的图结构，在图像元素之间全面建模稠密和稀疏关系，从而进一步增强了逻辑异常的检测，实验结果表明 SLSG 具有优越的异常检测性能。