The DOI matches its Crossref record; 73 of the 74 references it cites were found in a registry or an index.
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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- 10.1109/CVPR46437.2021.01466 · DOI · Paper page
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Deep Industrial Image Anomaly Detection: A Survey
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“The paper generates abnormal samples in feature level and perform better than DRAEM.”
InfluentialA Diffusion-Based Framework for Multi-Class Anomaly Detection
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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
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A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images
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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
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