The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Anomaly Detection via Reverse Distillation from One-Class Embedding
Open accessAbstract
Knowledge distillation (KD) achieves promising results on the challenging problem of unsupervised anomaly detection (AD). The representation discrepancy of anomalies in the teacher-student (T-S) model provides essential evidence for AD. However, using similar or identical architectures to build the teacher and student models in previous studies hinders the diversity of anomalous representations. To tackle this problem, we propose a novel T-S model consisting of a teacher encoder and a student decoder and introduce a simple yet effective “reverse distillation” paradigm accordingly. Instead of receiving raw images directly, the student network takes teacher model's one-class embedding as input and targets to restore the teacher's multi-scale representations. Inherently, knowledge distillation in this study starts from abstract, high-level presentations to low-level features. In addition, we introduce a trainable one-class bottleneck embedding (OCBE) module in our T-S model. The obtained compact embedding effectively preserves essential information on normal patterns, but aban-dons anomaly perturbations. Extensive experimentation on AD and one-class novelty detection benchmarks shows that our method surpasses SOTA performance, demonstrating our proposed approach's effectiveness and generalizability.
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Cited by
SPot-the-Difference Self-supervised Pre-training for Anomaly Detection and Segmentation
Zou, Y., Jeong, J., Pemula, L., Zhang, D., & Dabeer, O. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-20056-4_23 · Cited by 651
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
“RD4AD [14] Cosine Similarity ResNet The paper designs the teacher-student model of reverse distillation in a similar way to reconstruction.”
InfluentialRevisiting Reverse Distillation for Anomaly Detection
Tien, T. D., Nguyen, A. T., Tran, N. H., Huy, T. D., Duong, S. T. M., Nguyen, C. D. T., & Truong, S. Q. H. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.02348 · Cited by 268
Multimodal Industrial Anomaly Detection via Hybrid Fusion
Wang, Y., Peng, J., Zhang, J., Yi, R., Wang, Y., & Wang, C. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00776 · Cited by 246
A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images
Cui, Y., Liu, Z., & Lian, S. (2022) · arXiv · doi:10.48550/arxiv.2204.11161 · Cited by 154
Metadata
- Authors
- Deng, H., & Li, X.
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 9727-9736
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4312772600
- Cited by
- 892
- References
- 44
- FWCI
- 97.95
- Type
- conference paper
- Language
- en
- Open access
- green
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