Funder
Beijing Institute of Technology Research Fund Program for Young Scholars
Reference62 articles.
1. Ganomaly: Semi-supervised anomaly detection via adversarial training;Akcay,2019
2. Bae, J., Lee, J.-H., Kim, S., 2023. Pni: industrial anomaly detection using position and neighborhood information. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 6373–6383.
3. Bergmann, P., Fauser, M., Sattlegger, D., Steger, C., 2019. MVTec AD–A comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9592–9600.
4. Bergmann, P., Fauser, M., Sattlegger, D., Steger, C., 2020. Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4183–4192.
5. The mvtec 3d-ad dataset for unsupervised 3d anomaly detection and localization;Bergmann,2021