Papers › SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and...
SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classification
Minghui Yang, Jing Liu, Zhiwei Yang, Zhaoyang Wu
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.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Detection | MVTec LOCO AD | SLSG | Avg. Detection AUROC | 90.3 | #8 of 40 | Archive leaderboard | report |
| Anomaly Detection | MVTec LOCO AD | SLSG | Detection AUROC (only logical) | 89.6 | #8 of 40 | Archive leaderboard | report |
| Anomaly Detection | MVTec LOCO AD | SLSG | Detection AUROC (only structural) | 91.4 | #8 of 40 | Archive leaderboard | report |
| Anomaly Detection | MVTec LOCO AD | SLSG | Segmentation AU-sPRO (until FPR 5%) | 67.3 | #8 of 40 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections