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Recent semi-supervised anomaly detection methods that are trained using small labeled anomaly examples and large unlabeled data (mostly normal data) have shown largely improved performance over unsupervised methods.
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Wilcoxon signed-rank test
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Learning classifiers from only positive and unlabeled data. In KDD . ACM, 213–220
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Mining unexpected temporal associations: applications in detecting adverse drug reactions
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Outlier detection using nonconvex penalized regression
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Isolation-based anomaly detection
Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. 2012 · 2012
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FRaC: a feature-modeling approach for semi-supervised and unsupervised anomaly detection
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Area under the precision-recall curve: point estimates and confidence intervals. In ECML/PKDD . Springer, 451–466
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Toward supervised anomaly detection
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k-zero day safety: A network security metric for measuring the risk of unknown vulnerabilities
Lingyu Wang, Sushil Jajodia, Anoop Singhal, Pengsu Cheng, and Steven Noel. 2013 · 2013
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Subsampling for efficient and effective unsupervised outlier detection ensembles. In KDD . ACM, 428–436
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Guilt by association: Large scale malware detection by mining file-relation graphs. In KDD . 1524–1533
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UNSW-NB15: a comprehensive data set for network intrusion detection systems. In Military Communications and Information Systems Conference, 2015 . 1–6
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Matching networks for one shot learning. In NIPS . 3630–3638
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Outlier analysis
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Outlier detection with autoencoder ensembles. In SDM . SIAM, 90–98
Jinghui Chen, Saket Sathe, Charu Aggarwal, and Deepak Turaga. 2017 · 2017
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A semisupervised approach to the detection and characterization of outliers in categorical data
Dino Ienco, Ruggero G Pensa, and Rosa Meo. 2017 · 2017
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Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In IPMI . Springer, Cham, 146–157
Thomas Schlegl, Philipp Seeböck, Sebastian M Waldstein, Ursula Schmidt-Erfurth, and Georg Langs. 2017 · 2017
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Prototypical networks for few-shot learning. In NeurIPS . 4077–4087
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Robust linear regression: A review and comparison
Chun Yu and Weixin Yao. 2017 · 2017
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PU learning in payload-based web anomaly detection. In 2018 Third International Conference on Security of Smart Cities, Industrial Control System and Communications (SSIC) . IEEE, 1–5
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Deep one-class classification. In ICML . 4390–4399
Lukas Ruff, Nico Görnitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Robert Vandermeulen, Alexander Binder, Emmanuel Müller, and Marius Kloft. 2018 · 2018
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Learning memory-guided normality for anomaly detection. In CVPR . 14372–14381
Hyunjong Park, Jongyoun Noh, and Bumsub Ham. 2020 · 2020
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Class prior estimation in active positive and unlabeled learning. In Proceedings of the 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI 2020) . IJCAI-PRICAI, 2915–2921
Lorenzo Perini, Vincent Vercruyssen, and Jesse Davis. 2020 · 2020
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Deep Semi-Supervised Anomaly Detection. In ICLR
Lukas Ruff, Robert A Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, and Marius Kloft. 2020 · 2020
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Activation anomaly analysis. In ECML/PKDD . Springer, 69–84
Philip Sperl, Jan-Philipp Schulze, and Konstantin Böttinger. 2020 · 2020
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Unsupervised representation learning by predicting random distances
Hu Wang, Guansong Pang, Chunhua Shen, and Congbo Ma. 2020a · 2020
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Efficient training for positive unlabeled learning
Emanuele Sansone, Francesco GB De Natale, and Zhi-Hua Zhou. 2018 · 2018
Cited alongside, same era.
Learning to compare: Relation network for few-shot learning. In CVPR . 1199–1208
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales. 2018 · 2018
Cited alongside, same era.
Semi-supervised anomaly detection with an application to water analytics. In ICDM , Vol. 2018. IEEE, 527–536
Vincent Vercruyssen, Wannes Meert, Gust Verbruggen, Koen Maes, Ruben Baumer, and Jesse Davis. 2018 · 2018
Cited alongside, same era.
Adversarially Learned Anomaly Detection. In ICDM . IEEE, 727–736
Houssam Zenati, Manon Romain, Chuan-Sheng Foo, Bruno Lecouat, and Vijay Chandrasekhar. 2018 · 2018
Cited alongside, same era.
Anomaly detection with partially observed anomalies. In WWW Companion . 639–646
Ya-Lin Zhang, Longfei Li, Jun Zhou, Xiaolong Li, and Zhi-Hua Zhou. 2018 · 2018
Cited alongside, same era.
Xgbod: improving supervised outlier detection with unsupervised representation learning. In 2018 International Joint Conference on Neural Networks (IJCNN) . IEEE, 1–8
Yue Zhao and Maciej K Hryniewicki. 2018 · 2018
Cited alongside, same era.
Deep autoencoding Gaussian mixture model for unsupervised anomaly detection. In ICLR
Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen. 2018 · 2018
Cited alongside, same era.
Generalizing from a few examples: A survey on few-shot learning
Yaqing Wang, Quanming Yao, James T Kwok, and Lionel M Ni. 2020b · 2020
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Few-shot Network Anomaly Detection via Cross-network Meta-learning. In WebConf . 2448–2456
Kaize Ding, Qinghai Zhou, Hanghang Tong, and Huan Liu. 2021 · 2021
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Learning prototype representations across few-shot tasks for event detection. In EMNLP . 5270–5277
Viet Lai, Franck Dernoncourt, and Thien Huu Nguyen. 2021 · 2021
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Explainable deep one-class classification. In ICLR
Philipp Liznerski, Lukas Ruff, Robert A Vandermeulen, Billy Joe Franks, Marius Kloft, and Klaus-Robert Müller. 2021 · 2021
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Explainable Deep Few-shot Anomaly Detection with Deviation Networks
Guansong Pang, Choubo Ding, Chunhua Shen, and Anton van den Hengel. 2021a · 2021
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Deep Learning for Anomaly Detection: A Review
Guansong Pang, Chunhua Shen, Longbing Cao, and Anton van den Hengel. 2021b · 2021
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Panda: Adapting pretrained features for anomaly detection and segmentation. In CVPR . 2806–2814
Tal Reiss, Niv Cohen, Liron Bergman, and Yedid Hoshen. 2021 · 2021
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Learning and evaluating representations for deep one-class classification. In ICLR
Kihyuk Sohn, Chun-Liang Li, Jinsung Yoon, Minho Jin, and Tomas Pfister. 2021 · 2021
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Deep Clustering based Fair Outlier Detection. In KDD . 1481–1489
Hanyu Song, Peizhao Li, and Hongfu Liu. 2021 · 2021
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Weakly Supervised Anomaly Detection Based on Two-Step Cyclic Iterative PU Learning Strategy
Dongyue Chen, Xinyue Tantai, Xingya Chang, Miaoting Tian, and Tong Jia. 2022a · 2022
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Catching both gray and black swans: Open-set supervised anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 7388–7398
Choubo Ding, Guansong Pang, and Chunhua Shen. 2022 · 2022
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Estimating the Contamination Factor’s Distribution in Unsupervised Anomaly Detection
Lorenzo Perini, Paul Buerkner, and Arto Klami. 2022a · 2022
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Towards total recall in industrial anomaly detection. In CVPR . 14318–14328
Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, and Peter Gehler. 2022 · 2022
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Deep anomaly detection with self-supervised learning and adversarial training
Xianchao Zhang, Jie Mu, Xiaotong Zhang, Han Liu, Linlin Zong, and Yuangang Li. 2022 · 2022
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Feature encoding with autoencoders for weakly supervised anomaly detection
Yingjie Zhou, Xucheng Song, Yanru Zhang, Fanxing Liu, Ce Zhu, and Lingqiao Liu. 2022b · 2022
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Weakly supervised anomaly detection: A survey
Minqi Jiang, Chaochuan Hou, Ao Zheng, Xiyang Hu, Songqiao Han, Hailiang Huang, Xiangnan He, Philip S Yu, and Yue Zhao. 2023 · 2023
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Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection. In KDD . 2041–2050
Guansong Pang, Longbing Cao, Ling Chen, and Huan Liu. 2018 · 2050
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