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Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection
Open accessAbstract
Despite most existing anomaly detection studies assume the availability of normal training samples only, a few labeled anomaly examples are often available in many real-world applications, such as defect samples identified during random quality inspection, lesion images confirmed by radiologists in daily medical screening, etc. These anomaly examples provide valuable knowledge about the application-specific abnormality, enabling significantly improved detection of similar anomalies in some recent models. However, those anomalies seen during training often do not illustrate every possible class of anomaly, rendering these models ineffective in generalizing to unseen anomaly classes. This paper tackles open-set supervised anomaly detection, in which we learn detection models using the anomaly examples with the objective to detect both seen anomalies (‘gray swans’) and unseen anomalies (‘black swans’). We propose a novel approach that learns disentangled representations of abnormalities illustrated by seen anomalies, pseudo anomalies, and latent residual anomalies (i.e., samples that have unusual residuals compared to the normal data in a latent space), with the last two abnormalities designed to detect unseen anomalies. Extensive experiments on nine real-world anomaly detection datasets show superior performance of our model in detecting seen and unseen anomalies under diverse settings. Code and data are available at: https://github.com/choubo/DRA
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Robust Fraud Detection via Supervised Contrastive Learning
Vinay, M. S., Yuan, S., & Wu, X. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386925 · Cited by 6
ViGLAD: Vision Graph Neural Networks for Logical Anomaly Detection
Zoghlami, F., Bazazian, D., Masala, G. L., Gianni, M., & Khan, A. (2024) · IEEE Access · doi:10.1109/access.2024.3502514 · Cited by 4
Dual Memory Networks Guided Reverse Distillation for Unsupervised Anomaly Detection
Tran, C., Pham, L. H., Tran, D. N., Ho, Q., & Jeon, J. W. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0960-4_22 · Cited by 4
Guo, L., & Lv, F. (2025) · Machines · doi:10.3390/machines13080712 · Cited by 4
Anomaly Detection and Segmentation in Carotid Ultrasound Images Using Hybrid Stable AnoGAN
Saboori, A., Öhberg, F., Näslund, U., & Grönlund, C. (2025) · IEEE Access · doi:10.1109/access.2025.3611327 · Cited by 4
AnomalyControl: Highly-Aligned Anomalous Image Generation with Controlled Diffusion Model
Duan, Y., Xu, W., Wu, Q., Xie, G.-S., Zhao, F., & Shan, C. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755274 · Cited by 4
Deviation capture networks for anomaly detection
Yan, F., Cheng, J., Feng, J., Lu, Y., Zhang, W., Jiang, X., & Xu, M. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103886 · Cited by 3
A zero-shot anomaly detection network with patch-augmented prompts and test-time adaptation
Zhu, H., Zhao, C., Yuan, Y., & Liu, M. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113525 · Cited by 3
Metadata
- Authors
- Ding, C., Pang, G., & Shen, C.
- Affiliations
- The University of Adelaide (AU)
- Singapore Management University (SG)
- Zhejiang University (CN)
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 7378-7388
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4312289809
- Cited by
- 197
- References
- 74
- Citations by year
- 2026: 43 · 2025: 66 · 2024: 58 · 2023: 30
- FWCI
- 21.23
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Keywords
- supervised anomaly detection, black swan, disentangled representations, medical image anomaly detection, defect detection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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