  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.
