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
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,889
He, H., & Garcia, E. A. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.239 · Cited by 10,359
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Xiao, H., Rasul, K., & Vollgraf, R. (2017) · arXiv · doi:10.48550/arxiv.1708.07747 · Cited by 6,108
CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features
Yun, S., Han, D., Chun, S., Oh, S. J., Yoo, Y., & Choe, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00612 · Cited by 5,400
Deep Learning for Anomaly Detection: A Review
Pang, G., Shen, C., Cao, L., & Hengel, A. V. D. (2022) · ACM Computing Surveys · doi:10.1145/3439950 · Cited by 2,837
Self-citationThe Black Swan: The Impact of the Highly Improbable
(2008) · Library Review · doi:10.1108/00242530810854080 · Cited by 2,465
Real-World Anomaly Detection in Surveillance Videos
Sultani, W., Chen, C., & Shah, M. A. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00678 · Cited by 2,248
MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection
Bergmann, P., Fauser, M., Sattlegger, D., & Steger, C. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00982 · Cited by 2,203
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
Hendrycks, D., & Gimpel, K. (2016) · arXiv · doi:10.48550/arxiv.1610.02136 · Cited by 1,968
Gong, D., Liu, L., Le, V., Saha, B., Mansour, M. R., Venkatesh, S., & Van Den Hengel, A. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00179 · Cited by 1,930
Towards Open Set Deep Networks
Bendale, A., & Boult, T. E. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.173 · Cited by 1,799
Ruff, L., Vandermeulen, R. A., Görnitz, N., Deecke, L., Siddiqui, S. A., Binder, A., Müller, E., & Kloft, M. (2018) · International Conference on Machine Learning · Cited by 1,719
Deep Learning for Anomaly Detection: A Review
Pang, G., Shen, C., Cao, L., et al. (2020) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · doi:10.48550/arxiv.2007.02500 · Cited by 1,718
Self-citationf-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks
Schlegl, T., Seeböck, P., Waldstein, S. M., Langs, G., & Schmidt-Erfurth, U. M. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.010 · Cited by 1,645
Anomaly Detection with Robust Deep Autoencoders
Zhou, C., & Paffenroth, R. C. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098052 · Cited by 1,607
Scheirer, W. J., de Rezende Rocha, A., Sapkota, A., & Boult, T. E. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.256 · Cited by 1,573
Large-Scale Long-Tailed Recognition in an Open World
Liu, Z., Miao, Z., Zhan, X., Wang, J., Gong, B., & Yu, S. X. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00264 · Cited by 1,198
A Survey of Predictive Modeling on Imbalanced Domains
Branco, P., Torgo, L., & Ribeiro, R. P. (2017) · ACM Computing Surveys · doi:10.1145/2907070 · Cited by 1,129
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
Li, C.-L., Sohn, K., Yoon, J., & Pfister, T. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00954 · Cited by 1,107
Learning Memory-Guided Normality for Anomaly Detection
Park, H., Noh, J., & Ham, B. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01438 · Cited by 1,034
Segmentation-based deep-learning approach for surface-defect detection
Tabernik, D., Šela, S., Skvarč, J., & Skočaj, D. (2019) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-019-01476-x · Cited by 996
Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent Embeddings
Bergmann, P., Fauser, M., Sattlegger, D., & Steger, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00424 · Cited by 990
Deep Anomaly Detection with Outlier Exposure
Hendrycks, D., Mazeika, M., & Dietterich, T. (2018) · arXiv · doi:10.48550/arxiv.1812.04606 · Cited by 932
Adversarially Learned One-Class Classifier for Novelty Detection
Sabokrou, M., Khalooei, M., Fathy, M., & Adeli, E. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00356 · Cited by 765
HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy
Borgli, H., Thambawita, V. L., Smedsrud, P. H., Hicks, S. A., Jha, D., Eskeland, S. L., Randel, K. R., Pogorelov, K., Lux, M., Dang‐Nguyen, D., et al. (2020) · Scientific Data · doi:10.1038/s41597-020-00622-y · Cited by 626
OCGAN: One-Class Novelty Detection Using GANs With Constrained Latent Representations
Perera, P., Nallapati, R., & Xiang, B. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00301 · Cited by 608
Automatic classification of defective photovoltaic module cells in electroluminescence images
Deitsch, S., Christlein, V., Berger, S., Buerhop‐Lutz, C., Maier, A., Gallwitz, F., & Rieß, C. (2019) · Solar Energy · doi:10.1016/j.solener.2019.02.067 · Cited by 607
Patch SVDD: Patch-Level SVDD for Anomaly Detection and Segmentation
Yi, J., & Yoon, S. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-69544-6_23 · Cited by 551
Revisiting multiple instance neural networks
Wang, X., Yan, Y., Tang, P., Bai, X., & Liu, W. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.08.026 · Cited by 506
Anomaly Detection using One-Class Neural NetworksPreprint
Chalapathy, R., Menon, A. K., & Chawla, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.06360 · Cited by 497
Deep Anomaly Detection with Deviation Networks
Pang, G., Shen, C., & van den Hengel, A. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330871 · Cited by 495
Self-citationDeep Anomaly Detection Using Geometric TransformationsPreprint
Golan, I., & El‐Yaniv, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.10917 · Cited by 479
Likelihood Ratios for Out-of-Distribution DetectionPreprint
Ren, J., Liu, P. J., Fertig, E., Snoek, J., Poplin, R. E., DePristo, M. A., Dillon, J. V., & Lakshminarayanan, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.02845 · Cited by 456
Learning Deep Features for One-Class Classification
Perera, P., & Patel, V. M. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2917862 · Cited by 399
Toward Supervised Anomaly Detection
Görnitz, N., Kloft, M., Rieck, K., & Brefeld, U. (2013) · Journal of Artificial Intelligence Research · doi:10.1613/jair.3623 · Cited by 397
Viral pneumonia screening on chest X-rays using confidence-aware anomaly detection
Zhang, J., Xie, Y., Pang, G., Liao, Z., Verjans, J., Li, W., Sun, Z., He, J., Li, Y., Shen, C., et al. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3040950 · Cited by 389
Self-citationDeep Semi-Supervised Anomaly Detection
Ruff, L., Vandermeulen, R. A., Görnitz, N., Binder, A., Müller, E., Müller, K.-R., & Kloft, M. (2019) · arXiv · doi:10.48550/arxiv.1906.02694 · Cited by 388
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning
Georgescu, M.-I., Bărbălau, A., Ionescu, R. T., Khan, F. S., Popescu, M., & Shah, M. A. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01255 · Cited by 378
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
Tack, J., Mo, S., Jeong, J., & Shin, J. (2020) · arXiv · doi:10.48550/arxiv.2007.08176 · Cited by 351
Unmasking the Abnormal Events in Video
Ionescu, R. T., Smeureanu, S., Alexe, B., & Popescu, M. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.315 · Cited by 349
Attention Based Glaucoma Detection: A Large-Scale Database and CNN Model
Li, L., Xu, M., Wang, X., Jiang, L. W., & Liu, H. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01082 · Cited by 301
PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
Reiss, T., Cohen, N., Bergman, L., & Hoshen, Y. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00283 · Cited by 295
Classification-Based Anomaly Detection for General DataPreprint
Bergman, L., & Hoshen, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.02359 · Cited by 294
Learning Placeholders for Open-Set Recognition
Zhou, D.-W., Ye, H.-J., & Zhan, D. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00438 · Cited by 268
Attention Guided Anomaly Localization in Images
Venkataramanan, S., Peng, K.-C., Singh, R. V., & Mahalanobis, A. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58520-4_29 · Cited by 247
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
Pang, G., Cao, L., Chen, L., & Liu, H. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220042 · Cited by 234
Self-citationGraph Embedded Pose Clustering for Anomaly Detection
Markovitz, A., Sharir, G., Friedman, I., Zelnik-Manor, L., & Avidan, S. (2019) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr42600.2020.01055 · Cited by 222
Counterfactual Zero-Shot and Open-Set Visual Recognition
Yue, Z., Wang, T., Sun, Q., Hua, X., & Zhang, H. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01515 · Cited by 221
Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection
Hou, J., Zhang, Y., Zhong, Q., Xie, D., Pu, S., & Zhou, H. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00867 · Cited by 213
Old Is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm
Zaheer, M. Z., Lee, J.-H., Astrid, M., & Lee, S. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01419 · Cited by 205
Zaheer, M. Z., Mahmood, A., Astrid, M., & Lee, S.-I. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58542-6_22 · Cited by 202
Explainable Deep One-Class Classification
Liznerski, P., Ruff, L., Vandermeulen, R. A., Franks, B. J., Kloft, M., & Müller, K.-R. (2020) · arXiv · doi:10.48550/arxiv.2007.01760 · Cited by 185
MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space
Huang, R., & Li, Y. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00860 · Cited by 183
A Public Fabric Database for Defect Detection Methods and Results
Silvestre-Blanes, J., Albero-Albero, T., Miralles, I., Pérez-Lloréns, R., & Moreno, J. (2019) · Autex Research Journal · doi:10.2478/aut-2019-0035 · Cited by 179
Pixel-wise Anomaly Detection in Complex Driving Scenes
Biase, G. D., Blum, H., Siegwart, R., & Cadena, C. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01664 · Cited by 163
Learning and Evaluating Representations for Deep One-class ClassificationPreprint
Sohn, K., Li, C., Yoon, J., Jin, M., & Pfister, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.02578 · Cited by 162
Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images
Zhou, K., Xiao, Y., Yang, J., Cheng, J., Liu, W., Luo, W., Gu, Z., Liu, J., & Gao, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58565-5_22 · Cited by 146
Margin Learning Embedded Prediction for Video Anomaly Detection with A Few Anomalies
Liu, W., Luo, W., Li, Z., Zhao, P., & Gao, S. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/419 · Cited by 136
Deep One-Class Classification via Interpolated Gaussian Descriptor
Chen, Y., Tian, Y., Pang, G., & Carneiro, G. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19915 · Cited by 123
Self-citationToward Deep Supervised Anomaly Detection
Pang, G., van den Hengel, A., Shen, C., & Cao, L. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467417 · Cited by 121
Self-citationExplainable Deep Few-shot Anomaly Detection with Deviation NetworksPreprint
Pang, G., Ding, C., Shen, C., & van den Hengel, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.00462 · Cited by 98
Self-citationTian, Y., Liu, Y., Pang, G., Liu, F., Chen, Y., & Carneiro, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19842-7_15 · Cited by 91
Self-citationMOOD: Multi-level Out-of-distribution Detection
Lin, Z., Roy, S. D., & Li, Y. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01506 · Cited by 90
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network
Wang, S., Zeng, Y., Liu, X., Zhu, E., Yin, J., Xu, C., & Kloft, M. (2019) · neural information processing systems · Cited by 89
Glancing at the Patch: Anomaly Localization with Global and Local Feature Comparison
Wang, S., Wu, L., Cui, L., & Shen, Y. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00032 · Cited by 77
Tian, Y., Pang, G., Liu, F., Chen, Y., Shin, S. H., Verjans, J. W., Singh, R., & Carneiro, G. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-87240-3_13 · Cited by 77
Self-citationOut-of-Distribution Detection Using Union of 1-Dimensional Subspaces
Zaeemzadeh, A., Bisagno, N., Sambugaro, Z., Conci, N., Rahnavard, N., & Shah, M. A. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00933 · Cited by 62
Kerner, H., Wagstaff, K. L., Bue, B., Wellington, D., Jacob, S., Horton, P., Bell, J. F., III, Kwan, C., & Amor, H. B. (2020) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-020-00697-6 · Cited by 45
Major Accidents (Gray Swans) Likelihood Modeling Using Accident Precursors and Approximate Reasoning
Khakzad, N., Khan, F. I., & Amyotte, P. R. (2015) · Risk Analysis · doi:10.1111/risa.12337 · Cited by 32
Weakly Supervised Learning for Industrial Optical Inspection
Wieler, M., Hahn, T., & Hamprecht, F. A. (2007) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.8086135 · Cited by 27
Wang, P., Liu, L., Shen, C., Huang, Z. H., van den Hengel, A., & Shen, H. T. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.174 · Cited by 9
Self-citation
Research works printed but not linked: 1
- 10.1109/CVPR46437.2021.01466 · DOI · Paper page
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
“The paper generates abnormal samples in feature level and perform better than DRAEM.”
InfluentialA 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
A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images
Cui, Y., Liu, Z., & Lian, S. (2023) · IEEE Access · doi:10.1109/access.2023.3282993 · Cited by 185
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 ›
Are you an author, or do you see a problem with this page? Request a review
