2023

Weakly Supervised Anomaly Detection: A Survey

Jiang, Minqi, Hou, Chaochuan, Zheng, Ao et al.

Understand

Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news.

However, obtaining complete, accurate, and precise labels for AD tasks can be expensive and challenging due to the cost and difficulties in data annotation.
To address this issue, researchers have developed AD methods that can work with incomplete, inexact, and inaccurate supervision, collectively summarized as weakly supervised anomaly detection (WSAD) methods.
In this study, we present the first comprehensive survey of WSAD methods by categorizing them into the above three weak supervision settings across four data modalities (i.e., tabular, graph, time-series, and image/video data).

Built on

Snare: a link analytic system for graph labeling and risk detection
Mary McGlohon, Stephen Bay, Markus G Anderle, David M Steier, and Christos Faloutsos · 2009
Earlier work this paper cites.
Outlier Analysis
Charu C. Aggarwal · 2013
Earlier work this paper cites.
Classification in the presence of label noise: a survey
Benoît Frénay and Michel Verleysen · 2013
Earlier work this paper cites.
Toward supervised anomaly detection
Nico Görnitz, Marius Kloft, Konrad Rieck, and Ulf Brefeld · 2013
Earlier work this paper cites.
Learning outlier ensembles: The best of both worlds–supervised and unsupervised
Barbora Micenková, Brian McWilliams, and Ira Assent · 2014
Earlier work this paper cites.
Guilt by association: large scale malware detection by mining file-relation graphs
Acar Tamersoy, Kevin Roundy, and Duen Horng Chau · 2014
Earlier work this paper cites.
Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
Earlier work this paper cites.
Incorporating expert feedback into active anomaly discovery
Shubhomoy Das, Weng-Keen Wong, Thomas Dietterich, Alan Fern, and Andrew Emmott · 2016
Earlier work this paper cites.
Adversarial feature learning
Original
Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2016
Earlier work this paper cites.
Temporal ensembling for semi-supervised learning
Original
Samuli Laine and Timo Aila · 2016
Earlier work this paper cites.
Multiple instance learning: A survey of problem characteristics and applications
Marc-André Carbonneau, Veronika Cheplygina, Eric Granger, and Ghyslain Gagnon · 2018
Earlier work this paper cites.
Learning representations of ultrahigh-dimensional data for random distance-based outlier detection
Guansong Pang, Longbing Cao, Ling Chen, and Huan Liu · 2018
Earlier work this paper cites.
Deep one-class classification
Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft · 2018
Earlier work this paper cites.
Real-world anomaly detection in surveillance videos
Waqas Sultani, Chen Chen, and Mubarak Shah · 2018
Earlier work this paper cites.
Xgbod: improving supervised outlier detection with unsupervised representation learning
Yue Zhao and et al · 2018
Earlier work this paper cites.
A brief introduction to weakly supervised learning
Zhi-Hua Zhou · 2018
Earlier work this paper cites.
Deep autoencoding gaussian mixture model for unsupervised anomaly detection
Original
Bo Zong, Qi Song, Martin Renqiang Min, et al · 2018
Earlier work this paper cites.
Interactive anomaly detection on attributed networks
Kaize Ding, Jundong Li, and Huan Liu · 2019
Earlier work this paper cites.
Learning on-the-job to re-rank anomalies from top-1 feedback
Hemank Lamba and Leman Akoglu · 2019
Earlier work this paper cites.
Deep weakly-supervised anomaly detection
Original
Guansong Pang, Chunhua Shen, Huidong Jin, and Anton van den Hengel · 2019
Earlier work this paper cites.
Deep anomaly detection with deviation networks
Guansong Pang, Chunhua Shen, and Anton van den Hengel · 2019
Earlier work this paper cites.

Similar

Deep semi-supervised anomaly detection
Lukas Ruff, Robert A Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, and Marius Kloft · 2019
Cited alongside, same era.
A semi-supervised graph attentive network for financial fraud detection
Daixin Wang, Jianbin Lin, Peng Cui, et al · 2019
Cited alongside, same era.
Temporal convolutional network with complementary inner bag loss for weakly supervised anomaly detection
Jiangong Zhang, Laiyun Qing, and Jun Miao · 2019
Cited alongside, same era.
Pyod: A python toolbox for scalable outlier detection
Yue Zhao, Zain Nasrullah, and Zheng Li · 2019
Cited alongside, same era.
Graph convolutional label noise cleaner: Train a plug-and-play action classifier for anomaly detection
Jia-Xing Zhong et al · 2019
Cited alongside, same era.
Motion-aware feature for improved video anomaly detection
Yi Zhu and Shawn Newsam · 2019
Cited alongside, same era.
Esad: End-to-end deep semi-supervised anomaly detection
Original
Chaoqin Huang, Fei Ye, Peisen Zhao, et al · 2020
Cited alongside, same era.
Anomaly detection with inexact labels
Tomoharu Iwata, Machiko Toyoda, Shotaro Tora, and Naonori Ueda · 2020
Cited alongside, same era.
Weakly supervised video anomaly detection via center-guided discriminative learning
Boyang Wan, Yuming Fang, Xue Xia, and Jiajie Mei · 2020
Cited alongside, same era.
A self-reasoning framework for anomaly detection using video-level labels
Muhammad Zaigham Zaheer, Arif Mahmood, Hochul Shin, and Seung-Ik Lee · 2020
Cited alongside, same era.
Meta-aad: Active anomaly detection with deep reinforcement learning
Daochen Zha, Kwei-Herng Lai, Mingyang Wan, and Xia Hu · 2020
Cited alongside, same era.
Indirectly supervised anomaly detection of clinically meaningful health events from smart home data
Jessamyn Dahmen and Diane J Cook · 2021
Cited alongside, same era.

Then

Toward deep supervised anomaly detection: Reinforcement learning from partially labeled anomaly data
Guansong Pang, Anton van den Hengel, Chunhua Shen, and Longbing Cao · 2021
Later among the works it cites.
Dance with self-attention: A new look of conditional random fields on anomaly detection in videos
Didik Purwanto, Yie-Tarng Chen, and Wen-Hsien Fang · 2021
Later among the works it cites.
Weakly-supervised video anomaly detection with robust temporal feature magnitude learning
Yu Tian, Guansong Pang, Yuanhong Chen, et al · 2021
Later among the works it cites.
Weakly-supervised spatio-temporal anomaly detection in surveillance video
Original
Jie Wu, Wei Zhang, Guanbin Li, et al · 2021
Later among the works it cites.
Cross-epoch learning for weakly supervised anomaly detection in surveillance videos
Shenghao Yu, Chong Wang, Qiaomei Mao, Yuqi Li, and Jiafei Wu · 2021
Later among the works it cites.
Fraud detection under multi-sourced extremely noisy annotations
Chuang Zhang, Qizhou Wang, et al · 2021
Later among the works it cites.
Automatic unsupervised outlier model selection
Yue Zhao, Ryan Rossi, and Leman Akoglu · 2021
Later among the works it cites.
Feature encoding with autoencoders for weakly supervised anomaly detection
Yingjie Zhou, Xucheng Song, Yanru Zhang, Fanxing Liu, Ce Zhu, and Lingqiao Liu · 2021
Later among the works it cites.
Multi-scale continuity-aware refinement network for weakly supervised video anomaly detection
Yiling Gong, Chong Wang, Xinmiao Dai, Shenghao Yu, Lehong Xiang, and Jiafei Wu · 2022
Later among the works it cites.
Adbench: Anomaly detection benchmark
Songqiao Han, Xiyang Hu, Hailiang Huang, Minqi Jiang, and Yue Zhao · 2022
Later among the works it cites.
A semi-supervised vae based active anomaly detection framework in multivariate time series for online systems
Tao Huang, Pengfei Chen, and Ruipeng Li · 2022
Later among the works it cites.
Self-training multi-sequence learning with transformer for weakly supervised video anomaly detection
Shuo Li, Fang Liu, and Licheng Jiao · 2022
Later among the works it cites.
Dual-mgan: An efficient approach for semi-supervised outlier detection with few identified anomalies
Zhe Li, Chunhua Sun, Chunli Liu, Xiayu Chen, Meng Wang, and Yezheng Liu · 2022
Later among the works it cites.
Bond: Benchmarking unsupervised outlier node detection on static attributed graphs
Kay Liu, Yingtong Dou, Yue Zhao, Philip Yu, et al · 2022
Later among the works it cites.
Anomaly detection by leveraging incomplete anomalous knowledge with anomaly-aware bidirectional gans
Original
Bowen Tian, Qinliang Su, and Jian Yin · 2022
Later among the works it cites.
Tranad: Deep transformer networks for anomaly detection in multivariate time series data
Shreshth Tuli, Giuliano Casale, and Nicholas R Jennings · 2022
Later among the works it cites.
Towards unsupervised hpo for outlier detection
Original
Yue Zhao and Leman Akoglu · 2022
Later among the works it cites.
Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting
Tian Zhou, Ziqing Ma, et al · 2022
Later among the works it cites.
Addressing heterophily in graph anomaly detection: A perspective of graph spectrum
Yuan Gao, Xiang Wang, et al · 2023
Closest in time.
Alleviating structural distribution shift in graph anomaly detection
Yuan Gao, Xiang Wang, et al · 2023
Closest in time.
ADMoE: Anomaly detection with mixture-of-experts from noisy labels
Yue Zhao, Guoqing Zheng, Subhabrata Mukherjee, et al · 2023
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…