The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Informative knowledge distillation for image anomaly segmentation
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“Previous teacher-student architecture anomaly detection methods suffer from overfitting as a result of inconsistency between neural network capacity and knowledge amount.”
From Deep Industrial Image Anomaly Detection: A Survey (2024) · cites this paper as [15]
Citations
Cites
Deep Residual Learning for Image Recognition
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Distilling the Knowledge in a Neural Network
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Advances in Neural Information Processing Systems 29
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Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2983686 · Cited by 4,894
Cited by
Sorted by citations
Deep Industrial Image Anomaly Detection: A Survey
Liu, J., Xie, G., Wang, J., Li, S., Wang, C., Zheng, F., & Jin, Y. (2024) · Machine Intelligence Research · doi:10.1007/s11633-023-1459-z · Cited by 463
“By incorporating the Context Similarity Loss (CSL) and Adaptive Hard Sample Mining (AHSM) modules, Informative Knowledge Distillation (IKD) [15] hopes to reduce overfitting.”
BackgroundMethodInfluentialA Diffusion-Based Framework for Multi-Class Anomaly Detection
He, H., Zhang, J., Chen, H., Chen, X., Li, Z., Chen, X., Wang, Y., Wang, C., & Xie, L. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i8.28690 · Cited by 271
AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model
Hu, T., Zhang, J., Yi, R., Du, Y., Chen, X., Liu, L., Wang, Y., & Wang, C. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i8.28696 · Cited by 192
AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection
Cao, Y., Zhang, J., Frittoli, L., Cheng, Y., Shen, W., & Boracchi, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72761-0_4 · Cited by 193
Self-citationCollaborative Discrepancy Optimization for Reliable Image Anomaly Localization
Cao, Y., Xu, X., Liu, Z., & Shen, W. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3241579 · Cited by 154
Self-citation
Metadata
- Authors
- Cao, Y., Wan, Q., Shen, W., & Gao, L.
- Affiliations
- State Key Laboratory of Digital Manufacturing Equipment and Technology (CN)
- Corresponding author
- Shen, Weiming
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2022-07
- Volume
- 248
- Pages
- 108846
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W4224999518
- Semantic Scholar
- 4caabcee97045960308fc8372a611d36211e5b14
- Cited by
- 130
- References
- 45
- Citations by year
- 2026: 25 · 2025: 35 · 2024: 49 · 2023: 19 · 2022: 2
- FWCI
- 8.64
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Knowledge-Based Systems (publishedVersion)
- Funders
- Fundamental Research Funds for the Central Universities (2021GCRC058)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- unsupervised anomaly detection, overfitting, hard sample mining, MVTec AD
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