【论文合集】Awesome Anomaly Detection

github：GitHub - bitzhangcy/Deep-Learning-Based-Anomaly-Detection

Anomaly Detection: The process of detectingdata instances that significantly deviate from the majority of the whole dataset.

Contributed by Chunyang Zhang.

Survey Papers

A survey of single-scene video anomaly detection. TPAMI, 2022. paperBharathkumar Ramachandra, Michael J. Jones, and Ranga Raju Vatsavai.
Deep learning for anomaly detection: A review. ACM Computing Surveys, 2022. paperGuansong Pang, Chunhua Shen, Longbing Cao, and Anton Van Den Hengel.
A unifying review of deep and shallow anomaly detection. Proceedings of the IEEE, 2020. paperLukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, GrÉgoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, and Klaus-robert MÜller.
A review on outlier/anomaly detection in time series data. ACM Computing Surveys, 2022. paperAne Blázquez-García, Angel Conde, Usue Mori, and Jose A. Lozano.
Anomaly detection in autonomous driving: A survey. CVPR, 2022. paperDaniel Bogdoll, Maximilian Nitsche, and J. Marius Zöllner.
A comprehensive survey on graph anomaly detection with deep learning. TKDE, 2021. paperXiaoxiao Ma, Jia Wu, Shan Xue, Jian Yang, Chuan Zhou, Quan Z. Sheng, and Hui Xiong, and Leman Akoglu.
Transformers in time series: A survey. arXiv, 2022. paperQingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, and Liang Sun.
A survey on explainable anomaly detection. arXiv, 2022. paperZhong Li, Yuxuan Zhu, and Matthijs van Leeuwen.
Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues. KBS, 2020. paperArwa Aldweesh, Abdelouahid Derhab, and Ahmed Z.Emam.
Deep learning-based anomaly detection in cyber-physical systems: Progress and oportunities. ACM Computing Surveys, 2022. paperYuan Luo, Ya Xiao, Long Cheng, Guojun Peng, and Danfeng (Daphne) Yao.
GAN-based anomaly detection: A review. Neurocomputing, 2022. paperXuan Xia, Xizhou Pan, Nan Lia, Xing He, Lin Ma, Xiaoguang Zhang, and Ning Ding.
Unsupervised anomaly detection in time-series: An extensive evaluation and analysis of state-of-the-art methods. arXiv, 2022. paperNesryne Mejri, Laura Lopez-Fuentes, Kankana Roy, Pavel Chernakov, Enjie Ghorbel, and Djamila Aouada.
Deep learning for time series anomaly detection: A survey. arXiv, 2022. paperZahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan, Charu C. Aggarwal, and Mahsa Salehi.
A survey of deep learning-based network anomaly detection. Cluster Computing, 2019. paperDonghwoon Kwon, Hyunjoo Kim, Jinoh Kim, Sang C. Suh, Ikkyun Kim, and Kuinam J. Kim.
Survey on anomaly detection using data mining techniques. Procedia Computer Science, 2015. paperShikha Agrawal and Jitendra Agrawal.
Graph based anomaly detection and description: A survey. Data Mining and Knowledge Discovery, 2015. paperLeman Akoglu, Hanghang Tong, and Danai Koutra.
Domain anomaly detection in machine perception: A system architecture and taxonomy. TPAMI, 2014. paperJosef Kittler, William Christmas, Teófilo de Campos, David Windridge, Fei Yan, John Illingworth, and Magda Osman.
Graph-based time-series anomaly detection: A Survey. arXiv, 2023. paperThi Kieu Khanh Ho, Ali Karami, and Narges Armanfard.
Weakly supervised anomaly detection: A survey. arXiv, 2023. paperMinqi Jiang, Chaochuan Hou, Ao Zheng, Xiyang Hu, Songqiao Han, Hailiang Huang, Xiangnan He, Philip S. Yu, and Yue Zhao.

Methodology

AutoEncoder

Graph regularized autoencoder and its application in unsupervised anomaly detection. TPAMI, 2022. paperImtiaz Ahmed, Travis Galoppo, Xia Hu, and Yu Ding.
Innovations autoencoder and its application in one-class anomalous sequence detection. JMLR, 2022. paperXinyi Wang and Lang Tong.
Autoencoders-A comparative analysis in the realm of anomaly detection. CVPR, 2022. paperSarah Schneider, Doris Antensteiner, Daniel Soukup, and Matthias Scheutz.
Attention guided anomaly localization in images. ECCV, 2020. paperShashanka Venkataramanan, Kuan-Chuan Peng, Rajat Vikram Singh, and Abhijit Mahalanobis.
Latent space autoregression for novelty detection. CVPR, 2018. paperDavide Abati, Angelo Porrello, Simone Calderara, and Rita Cucchiara.
Anomaly detection in time series with robust variational quasi-recurrent autoencoders. ICDM, 2018. paperTung Kieu, Bin Yang, Chenjuan Guo, Razvan-Gabriel Cirstea, Yan Zhao, Yale Song, and Christian S. Jensen.
Robust and explainable autoencoders for unsupervised time series outlier detection. ICDE, 2022. paperTung Kieu, Bin Yang, Chenjuan Guo, Christian S. Jensen, Yan Zhao, Feiteng Huang, and Kai Zheng.
Latent feature learning via autoencoder training for automatic classification configuration recommendation. KBS, 2022. paperLiping Deng and MingQing Xiao.
Deep autoencoding Gaussian mixture model for unsupervised anomaly detection. ICLR, 2018. paperBo Zongy, Qi Songz, Martin Renqiang Miny, Wei Chengy, Cristian Lumezanuy, Daeki Choy, and Haifeng Chen.
Anomaly detection with robust deep autoencoders. KDD, 2017. paperChong Zhou and Randy C. Paffenroth.
Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications. WWW, 2018. paperHaowen Xu, Wenxiao Chen, Nengwen Zhao,Zeyan Li, Jiahao Bu, Zhihan Li, Ying Liu, Youjian Zhao, Dan Pei, Yang Feng, Jie Chen, Zhaogang Wang, and Honglin Qiao.
Spatio-temporal autoencoder for video anomaly detection. MM, 2017. paperYiru Zhao, Bing Deng, Chen Shen, Yao Liu, Hongtao Lu, and Xiansheng Hua.
Learning discriminative reconstructions for unsupervised outlier removal. ICCV, 2015. paperYan Xia, Xudong Cao, Fang Wen, Gang Hua, and Jian Sun.
Outlier detection with autoencoder ensembles. ICDM, 2017. paperJinghui Chen, Saket Sathey, Charu Aggarwaly, and Deepak Turaga.
A study of deep convolutional auto-encoders for anomaly detection in videos. Pattern Recognition Letters, 2018. paperManassés Ribeiro, AndréEugênio Lazzaretti, and Heitor Silvério Lopes.
Classification-reconstruction learning for open-set recognition. CVPR, 2019. paperRyota Yoshihashi, Shaodi You, Wen Shao, Makoto Iida, Rei Kawakami, and Takeshi Naemura.
Making reconstruction-based method great again for video anomaly detection. ICDM, 2022. paperYizhou Wang, Can Qin, Yue Bai, Yi Xu, Xu Ma, and Yun Fu.
Two-stream decoder feature normality estimating network for industrial snomaly fetection. ICASSP, 2023. paperChaewon Park, Minhyeok Lee, Suhwan Cho, Donghyeong Kim, and Sangyoun Lee.
Synthetic pseudo anomalies for unsupervised video anomaly detection: A simple yet efficient framework based on masked autoencoder. ICASSP, 2023. paperXiangyu Huang, Caidan Zhao, Chenxing Gao, Lvdong Chen, and Zhiqiang Wu.

GAN

Stabilizing adversarially learned one-class novelty detection using pseudo anomalies. TIP, 2022. paperMuhammad Zaigham Zaheer, Jin-Ha Lee, Arif Mahmood, Marcella Astri, and Seung-Ik Lee.
GAN ensemble for anomaly detection. AAAI, 2021. paperHan, Xu, Xiaohui Chen, and Liping Liu.
Generative cooperative learning for unsupervised video anomaly detection. CVPR, 2022. paperZaigham Zaheer, Arif Mahmood, M. Haris Khan, Mattia Segu, Fisher Yu, and Seung-Ik Lee.
GAN-based anomaly detection in imbalance problems. ECCV, 2020. paperJunbong Kim, Kwanghee Jeong, Hyomin Choi, and Kisung Seo.
Old is gold: Redefining the adversarially learned one-class classifier training paradigm. CVPR, 2020. paperMuhammad Zaigham Zaheer, Jin-ha Lee, Marcella Astrid, and Seung-Ik Lee.
Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. IPMI, 2017. paperThomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein, Ursula Schmidt-Erfurth, and Georg Langs.
Adversarially learned anomaly detection. ICDM, 2018. paperHoussam Zenati, Manon Romain, Chuan-Sheng Foo, Bruno Lecouat, and Vijay Chandrasekhar.
BeatGAN: Anomalous rhythm detection using adversarially generated time series. IJCAI, 2019. paperBin Zhou, Shenghua Liu, Bryan Hooi, Xueqi Cheng, and Jing Ye.
Convolutional transformer based dual discriminator generative adversarial networks for video anomaly detection. MM, 2021. paperXinyang Feng, Dongjin Song, Yuncong Chen, Zhengzhang Chen, Jingchao Ni, and Haifeng Chen.
USAD: Unsupervised anomaly detection on multivariate time series. KDD, 2020. paperJulien Audibert, Pietro Michiardi, Frédéric Guyard, Sébastien Marti, and Maria A. Zuluaga.
Anomaly detection with generative adversarial networks for multivariate time series. ICLR, 2018. paperDan Li, Dacheng Chen, Jonathan Goh, and See-kiong Ng.
Efficient GAN-based anomaly detection. ICLR, 2018. paperHoussam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, and Vijay Ramaseshan Chandrasekhar.
GANomaly: Semi-supervised anomaly detection via adversarial training. ACCV, 2019. paperAkcay, Samet, Amir Atapour-Abarghouei, and Toby P. Breckon.
f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis, 2019. paperThomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein, Georg Langs, and Ursula Schmidt-Erfurth.
OCGAN: One-class novelty detection using GANs with constrained latent representations. CVPR, 2019. paperPramuditha Perera, Ramesh Nallapati, and Bing Xiang.
Adversarially learned one-class classifier for novelty detection. CVPR, 2018. paperMohammad Sabokrou, Mohammad Khalooei, Mahmood Fathy, and Ehsan Adeli.
Generative probabilistic novelty detection with adversarial autoencoders. NIPS, 2018. paperStanislav Pidhorskyi, Ranya Almohsen, Donald A. Adjeroh, and Gianfranco Doretto.
Image anomaly detection with generative adversarial networks. ECML PKDD, 2018. paperLucas Deecke, Robert Vandermeulen, Lukas Ruff, Stephan Mandt, and Marius Kloft.
RGI: Robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection. ICLR, 2023. paperShancong Mou, Xiaoyi Gu, Meng Cao, Haoping Bai, Ping Huang, Jiulong Shan, and Jianjun Shi.

Flow

OneFlow: One-class flow for anomaly detection based on a minimal volume region. TPAMI, 2022. paperLukasz Maziarka, Marek Smieja, Marcin Sendera, Lukasz Struski, Jacek Tabor, and Przemyslaw Spurek.
Comprehensive regularization in a bi-directional predictive network for video anomaly detection. AAAI, 2022. paperChengwei Chen, Yuan Xie, Shaohui Lin, Angela Yao, Guannan Jiang, Wei Zhang, Yanyun Qu, Ruizhi Qiao, Bo Ren, and Lizhuang Ma.
Future frame prediction network for video anomaly detection. TPAMI, 2022. paperWeixin Luo, Wen Liu, Dongze Lian, and Shenghua Gao.
Graph-augmented normalizing flows for anomaly detection of multiple time series. ICLR, 2022. paperEnyan Dai and Jie Chen.
Cloze test helps: Effective video anomaly detection via learning to complete video events. MM, 2020. paperGuang Yu, Siqi Wang, Zhiping Cai, En Zhu, Chuanfu Xu, Jianping Yin, and Marius Kloft.
A modular and unified framework for detecting and localizing video anomalies. WACV, 2022. paperKeval Doshi and Yasin Yilmaz.
Video anomaly detection with compact feature sets for online performance. TIP, 2017. paperRoberto Leyva, Victor Sanchez, and Chang-Tsun Li.
U-Flow: A U-shaped normalizing flow for anomaly detection with unsupervised threshold. arXiv, 2017. paperMatías Tailanian, Álvaro Pardo, and Pablo Musé.
Bi-directional frame interpolation for unsupervised video anomaly detection. WACV, 2023. paperHanqiu Deng, Zhaoxiang Zhang, Shihao Zou, and Xingyu Li.
AE-FLOW: Autoencoders with normalizing flows for medical images anomaly detection. ICLR, 2023. paperYuzhong Zhao, Qiaoqiao Ding, and Xiaoqun Zhang.
A video anomaly detection framework based on appearance-motion semantics representation consistency. ICASSP, 2023. paperXiangyu Huang, Caidan Zhao, and Zhiqiang Wu.

Diffusion Model

AnoDDPM: Anomaly detection with denoising diffusion probabilistic models using simplex noise. CVPR, 2022. paperJulian Wyatt, Adam Leach, Sebastian M. Schmon, and Chris G. Willcocks.
Diffusion models for medical anomaly detection. MICCAI, 2022. paperJulia Wolleb, Florentin Bieder, Robin Sandkühler, and Philippe C. Cattin.
DiffusionAD: Denoising diffusion for anomaly detection. arXiv, 2023. paperHui Zhang, Zheng Wang, Zuxuan Wu, Yugang Jiang.

Transformer

Video anomaly detection via prediction network with enhanced spatio-temporal memory exchange. ICASSP, 2022. paperGuodong Shen, Yuqi Ouyang, and Victor Sanchez.
TranAD: Deep transformer networks for anomaly detection in multivariate time series data. VLDB, 2022. paperShreshth Tuli, Giuliano Casale, and Nicholas R. Jennings.
Pixel-level anomaly detection via uncertainty-aware prototypical transformer. MM, 2022. paperChao Huang, Chengliang Liu, Zheng Zhang, Zhihao Wu, Jie Wen, Qiuping Jiang, and Yong Xu.
AddGraph: Anomaly detection in dynamic graph using attention-based temporal GCN. IJCAI, 2019. paperLi Zheng, Zhenpeng Li, Jian Li, Zhao Li, and Jun Gao.
Anomaly transformer: Time series anomaly detection with association discrepancy. ICLR, 2022. paperJiehui Xu, Haixu Wu, Jianmin Wang, and Mingsheng Long.
Constrained adaptive projection with pretrained features for anomaly detection. IJCAI, 2022. paperXingtai Gui, Di Wu, Yang Chang, and Shicai Fan.
Self-training multi-sequence learning with transformer for weakly supervised video anomaly detection. AAAI, 2022. paperShuo Li, Fang Liu, and Licheng Jiao.
Beyond outlier detection: Outlier interpretation by attention-guided triplet deviation network. WWW, 2021. paperHongzuo Xu, Yijie Wang, Songlei Jian, Zhenyu Huang, Yongjun Wang, Ning Liu, and Fei Li.
Framing algorithmic recourse for anomaly detection. KDD, 2022. paperDebanjan Datta, Feng Chen, and Naren Ramakrishnan.
Inpainting transformer for anomaly detection. ICIAP, 2022. paperJonathan Pirnay and Keng Chai.
Self-supervised and interpretable anomaly detection using network transformers. arXiv, 2022. paperDaniel L. Marino, Chathurika S. Wickramasinghe, Craig Rieger, and Milos Manic.
Anomaly detection in surveillance videos using transformer based attention model. arXiv, 2022. paperKapil Deshpande, Narinder Singh Punn, Sanjay Kumar Sonbhadra, and Sonali Agarwal.
Multi-contextual predictions with vision transformer for video anomaly detection. arXiv, 2022. paperJoo-Yeon Lee, Woo-Jeoung Nam, and Seong-Whan Lee.
Transformer based models for unsupervised anomaly segmentation in brain MR images. arXiv, 2022. paperAhmed Ghorbel, Ahmed Aldahdooh, Shadi Albarqouni, and Wassim Hamidouche.
HaloAE: An HaloNet based local transformer auto-encoder for anomaly detection and localization. arXiv, 2022. paperE. Mathian, H. Liu, L. Fernandez-Cuesta, D. Samaras, M. Foll, and L. Chen.
Generalizable industrial visual anomaly detection with self-induction vision transformer. arXiv, 2022. paperHaiming Yao and Xue Wang,.
VT-ADL: A vision transformer network for image anomaly detection and localization. ISIE, 2021. paperPankaj Mishra, Riccardo Verk, Daniele Fornasier, Claudio Piciarelli, and Gian Luca Foresti.

Representation Learning

Localizing anomalies from weakly-labeled videos. TIP, 2021. paperHui Lv, Chuanwei Zhou, Zhen Cui, Chunyan Xu, Yong Li, and Jian Yang.
PAC-Wrap: Semi-supervised PAC anomaly detection. KDD, 2022. paperShuo Li, Xiayan Ji, Edgar Dobriban, Oleg Sokolsky, and Insup Lee.
Effective end-to-end unsupervised outlier detection via inlier priority of discriminative network. NIPS, 2019. paperSiqi Wang, Yijie Zeng, Xinwang Liu, En Zhu, Jianping Yin, Chuanfu Xu, and Marius Kloft.
AnomalyHop: An SSL-based image anomaly localization method. ICVCIP, 2021. paperKaitai Zhang, Bin Wang, Wei Wang, Fahad Sohrab, Moncef Gabbouj, and C.-C. Jay Kuo.
Learning representations of ultrahigh-dimensional data for random distance-based outlier detection. KDD, 2018. paperGuansong Pang, Longbing Cao, Ling Chen, and Huan Liu.
Federated disentangled representation learning for unsupervised brain anomaly detection. NMI, 2022. paperCosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert, and Shadi Albarqouni.
DSR–A dual subspace re-projection network for surface anomaly detection. ECCV, 2022. paperVitjan Zavrtanik, Matej Kristan, and Danijel Skočaj.
LGN-Net: Local-global normality network for video anomaly detection. arXiv, 2022. paperMengyang Zhao, Yang Liu, Jing Liu, Di Li, and Xinhua Zeng.
Glancing at the patch: Anomaly localization with global and local feature comparison. CVPR, 2021. paperShenzhi Wang, Liwei Wu, Lei Cui, and Yujun Shen.
SPot-the-difference self-supervised pre-training for anomaly detection and segmentation. ECCV, 2022. paperYang Zou, Jongheon Jeong, Latha Pemula, Dongqing Zhang, and Onkar Dabeer.
SSD: A unified framework for self-supervised outlier detection. ICLR, 2021. paperVikash Sehwag, Mung Chiang, and Prateek Mittal.
NETS: Extremely fast outlier detection from a data stream via set-based processing. VLDB, 2019. paperSusik Yoon, Jae-Gil Lee, and Byung Suk Lee.
XGBOD: Improving supervised outlier detection with unsupervised representation learning. IJCNN, 2018. paperYue Zhao and Maciej K. Hryniewicki.
Red PANDA: Disambiguating anomaly detection by removing nuisance factors. ICLR, 2023. paperNiv Cohen, Jonathan Kahana, and Yedid Hoshen.
TimesNet: Temporal 2D-variation modeling for general time series analysis. ICLR, 2023. paperHaixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long.

Nonparametric Approach

Real-time nonparametric anomaly detection in high-dimensional settings. TPAMI, 2021. paperMehmet Necip Kurt, Yasin Yılmaz, and Xiaodong Wang.
Neighborhood structure assisted non-negative matrix factorization and its application in unsupervised point anomaly detection. JMLR, 2021. paperImtiaz Ahmed, Xia Ben Hu, Mithun P. Acharya, and Yu Ding.
Bayesian nonparametric submodular video partition for robust anomaly detection. CVPR, 2022. paperHitesh Sapkota and Qi Yu.

Reinforcement Learning

Towards experienced anomaly detector through reinforcement learning. AAAI, 2018. paperChengqiang Huang, Yulei Wu, Yuan Zuo, Ke Pei, and Geyong Min.
Sequential anomaly detection using inverse reinforcement learning. KDD, 2019. paperMin-hwan Oh and Garud Iyengar.
Toward deep supervised anomaly detection: Reinforcement learning from partially labeled anomaly data. KDD, 2021. paperGuansong Pang, Anton van den Hengel, Chunhua Shen, and Longbing Cao.
Automated anomaly detection via curiosity-guided search and self-imitation learning. TNNLS, 2021. paperYuening Li, Zhengzhang Chen, Daochen Zha, Kaixiong Zhou, Haifeng Jin, Haifeng Chen, and Xia Hu.
Meta-AAD: Active anomaly detection with deep reinforcement learning. ICDM, 2020. paperDaochen Zha, Kwei-Herng Lai, Mingyang Wan, and Xia Hu.

CNN

Self-supervised predictive convolutional attentive block for anomaly detection. CVPR, 2022. paperNicolae-Catalin Ristea, Neelu Madan, Radu Tudor Ionescu, Kamal Nasrollahi, Fahad Shahbaz Khan, Thomas B. Moeslund, and Mubarak Shah.
Catching both gray and black swans: Open-set supervised anomaly detection. CVPR, 2022. paperChoubo Ding, Guansong Pang, and Chunhua Shen.
Learning memory-guided normality for anomaly detection. CVPR, 2020. paperHyunjong Park, Jongyoun No, and Bumsub Ham.
CutPaste: Self-supervised learning for anomaly detection and localization. CVPR, 2021. paperChunliang Li, Kihyuk Sohn, Jinsung Yoon, and Tomas Pfister.
Object-centric auto-encoders and dummy anomalies for abnormal event detection in video. CVPR, 2019. paperRadu Tudor Ionescu, Fahad Shahbaz Khan, Mariana-Iuliana Georgescu, and Ling Shao.
Mantra-Net: Manipulation tracing network for detection and localization of image forgeries with anomalous features. CVPR, 2019. paperYue Wu, Wael AbdAlmageed, and Premkumar Natarajan.
Grad-CAM: Visual explanations from deep networks via gradient-based localization. ICCV, 2017. paperRamprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra.
A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. AAAI, 2019. paperChuxu Zhang, Dongjin Song, Yuncong Chen, Xinyang Feng, Cristian Lumezanu, Wei Cheng, Jingchao Ni, Bo Zong, Haifeng Chen, and Nitesh V. Chawla.
Real-world anomaly detection in surveillance videos. CVPR, 2018. paperWaqas Sultani, Chen Chen, and Mubarak Shah.
FastAno: Fast anomaly detection via spatio-temporal patch transformation. WACV, 2022. paperChaewon Park, MyeongAh Cho, Minhyeok Lee, and Sangyoun Lee.
Object class aware video anomaly detection through image translation. CRV, 2022. paperMohammad Baradaran and Robert Bergevin.
Anomaly detection in video sequence with appearance-motion correspondence. ICCV, 2019. paperTrong-Nguyen Nguyen and Jean Meunier.
Joint detection and recounting of abnormal events by learning deep generic knowledge. ICCV, 2017. paperRyota Hinami, Tao Mei, and Shin’ichi Satoh.
Deep-cascade: Cascading 3D deep neural networks for fast anomaly detection and localization in crowded scenes. TIP, 2017. paperMohammad Sabokrou, Mohsen Fayyaz, Mahmood Fathy, and Reinhard Klette.
Towards interpretable video anomaly detection. WACV, 2023. paperKeval Doshi and Yasin Yilmaz.
Lossy compression for robust unsupervised time-series anomaly detection. CVPR, 2023. paperChristopher P. Ley and Jorge F. Silva.
Learning second order local anomaly for general face forgery detection. CVPR, 2022. paperJianwei Fei, Yunshu Dai, Peipeng Yu, Tianrun Shen, Zhihua Xia, and Jian Weng.

Graph Neural Network

Graph convolutional label noise cleaner: Train a plug-and-play action classifier for anomaly detection. CVPR, 2019. paperJiaxing Zhong, Nannan Li, Weijie Kong, Shan Liu, Thomas H. Li, and Ge Li.
Towards open set video anomaly detection. ECCV, 2019. paperYuansheng Zhu, Wentao Bao, and Qi Yu.
Decoupling representation learning and classification for GNN-based anomaly detection. SIGIR, 2021. paperYanling Wan,, Jing Zhang, Shasha Guo, Hongzhi Yin, Cuiping Li, and Hong Chen.
Crowd-level abnormal behavior detection via multi-scale motion consistency learning. AAAI, 2023. paperLinbo Luo, Yuanjing Li, Haiyan Yin, Shangwei Xie, Ruimin Hu, and Wentong Cai.
Rethinking graph neural networks for anomaly detection. ICML, 2022. paperJianheng Tang, Jiajin Li, Ziqi Gao, and Jia Li.
Cross-domain graph anomaly detection via anomaly-aware contrastive alignment. AAAI, 2023. paperQizhou Wang, Guansong Pang, Mahsa Salehi, Wray Buntine, and Christopher Leckie.
A causal inference look at unsupervised video anomaly detection. AAAI, 2022. paperXiangru Lin, Yuyang Chen, Guanbin Li, and Yizhou Yu.
NetWalk: A flexible deep embedding approach for anomaly detection in dynamic networks. KDD, 2018. paperWenchao Yu, Wei Cheng, Charu C. Aggarwal, Kai Zhang, Haifeng Chen, and Wei Wang.
LUNAR: Unifying local outlier detection methods via graph neural networks. AAAI, 2022. paperAdam Goodge, Bryan Hooi, See-Kiong Ng, and Wee Siong Ng.
Series2Graph: Graph-based subsequence anomaly detection for time series. VLDB, 2022. paperPaul Boniol and Themis Palpanas.
Graph embedded pose clustering for anomaly detection. CVPR, 2020. paperAmir Markovitz, Gilad Sharir, Itamar Friedman, Lihi Zelnik-Manor, and Shai Avidan.
Fast memory-efficient anomaly detection in streaming heterogeneous graphs. KDD, 2016. paperEmaad Manzoor, Sadegh M. Milajerdi, and Leman Akoglu.
Raising the bar in graph-level anomaly detection. IJCAI, 2022. paperChen Qiu, Marius Kloft, Stephan Mandt, and Maja Rudolph.
SpotLight: Detecting anomalies in streaming graphs. KDD, 2018. paperDhivya Eswaran, Christos Faloutsos, Sudipto Guha, and Nina Mishra.
Graph anomaly detection via multi-scale contrastive learning networks with augmented view. AAAI, 2023. paperJingcan Duan, Siwei Wang, Pei Zhang, En Zhu, Jingtao Hu, Hu Jin, Yue Liu, and Zhibin Dong.
Counterfactual graph learning for anomaly detection on attributed networks. TKDE, 2023. paperChunjing Xiao, Xovee Xu, Yue Lei, Kunpeng Zhang, Siyuan Liu, and Fan Zhou.
Deep variational graph convolutional recurrent network for multivariate time series anomaly detection. ICML, 2022. paperWenchao Chen, Long Tian, Bo Chen, Liang Dai, Zhibin Duan, and Mingyuan Zhou.

Sparse Coding

Video anomaly detection with sparse coding inspired deep neural networks. TPAMI, 2021. paperWeixin Luo, Wen Liu, Dongze Lian, Jinhui Tang, Lixin Duan, Xi Peng, and Shenghua Gao.
Self-supervised sparse representation for video anomaly detection. ECCV, 2022. paperJhihciang Wu, Heyen Hsieh, Dingjie Chen, Chioushann Fuh, and Tyngluh Liu.
A revisit of sparse coding based anomaly detection in stacked RNN framework. ICCV, 2017. paperWeixin Luo, Wen Liu, and Shenghua Gao.
HashNWalk: Hash and random walk based anomaly detection in hyperedge streams. IJCAI, 2022. paperGeon Lee, Minyoung Choe, and Kijung Shin.
Fast abnormal event detection. IJCV, 2019. paperCewu Lu, Jianping Shi, Weiming Wang, and Jiaya Jia.

Support Vector

Patch SVDD: Patch-level SVDD for anomaly detection and segmentation. ACCV, 2020. paperJihun Yi and Sungroh Yoon.
Multiclass anomaly detector: The CS++ support vector machine. JMLR, 2020. paperAlistair Shilton, Sutharshan Rajasegarar, and Marimuthu Palaniswami.
Timeseries anomaly detection using temporal hierarchical one-class network. NIPS, 2020. paperLifeng Shen, Zhuocong Li, and James Kwok.
LOSDD: Leave-out support vector data description for outlier detection. arXiv, 2022. paperDaniel Boiar, Thomas Liebig, and Erich Schubert.
Anomaly detection using one-class neural networks. arXiv, 2018. paperRaghavendra Chalapathy, Aditya Krishna Menon, and Sanjay Chawla.
Deep graph stream SVDD: Anomaly detection in cyber-physical systems. PAKDD, 2023. paperEhtesamul Azim, Dongjie Wang, and Yanjie Fu.

OOD

Your out-of-distribution detection method is not robust! NIPS, 2022. paperMohammad Azizmalayeri, Arshia Soltani Moakhar, Arman Zarei, Reihaneh Zohrabi, Mohammad Taghi Manzuri, and Mohammad Hossein Rohban.
Exploiting mixed unlabeled data for detecting samples of seen and unseen out-of-distribution classes. AAAI, 2022. paperYixuan Sun and Wei Wang.
RankFeat: Rank-1 feature removal for out-of-distribution detection. AAAI, 2022. paperYue Song, Nicu Sebe, and Wei Wang.
Detect, distill and update: Learned DB systems facing out of distribution data. SIGMOD, 2023. paperMeghdad Kurmanji and Peter Triantafillou.
Beyond mahalanobis distance for textual OOD detection. NIPS, 2022. paperPierre Colombo, Eduardo Dadalto Câmara Gomes, Guillaume Staerman, Nathan Noiry, and Pablo Piantanida.
Exploring the limits of out-of-distribution detection. NIPS, 2021. paperStanislav Fort, Jie Ren, and Balaji Lakshminarayanan.
Is out-of-distribution detection learnable? ICLR, 2022. paperZhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, and Feng Liu.
Out-of-distribution detection is not all you need. NIPS, 2022. paperJoris Guerin, Kevin Delmas, Raul Sena Ferreira, and Jérémie Guiochet.
iDECODe: In-distribution equivariance for conformal out-of-distribution detection. AAAI, 2022. paperRamneet Kaur, Susmit Jha, Anirban Roy, Sangdon Park, Edgar Dobriban, Oleg Sokolsky, and Insup Lee.
Out-of-distribution detection using an ensemble of self supervised leave-out classifiers. ECCV, 2018. paperApoorv Vyas, Nataraj Jammalamadaka, Xia Zhu, Dipankar Das, Bharat Kaul, and Theodore L. Willke.
Self-supervised learning for generalizable out-of-distribution detection. AAAI, 2020. paperSina Mohseni, Mandar Pitale, JBS Yadawa, and Zhangyang Wang.
Augmenting softmax information for selective classification with out-of-distribution data. ACCV, 2022. paperGuoxuan Xia and Christos-Savvas Bouganis.
Robustness to spurious correlations improves semantic out-of-distribution detection. AAAI, 2023. paperLily H. Zhang and Rajesh Ranganath.
Non-parametric outlier synthesis. ICLR, 2023. paperLeitian Tao, Xuefeng Du, Jerry Zhu, and Yixuan Li.
Out-of-distribution detection with implicit outlier transformation. ICLR, 2023. paperQizhou Wang, Junjie Ye, Feng Liu, Quanyu Dai, Marcus Kalander, Tongliang Liu, Jianye Hao, and Bo Han.
Out-of-distribution representation learning for time series classification. ICLR, 2023. paperWang Lu, Jindong Wang, Xinwei Sun, Yiqiang Chen, and Xing Xie.
Out-of-distribution detection based on in-distribution data patterns memorization with modern Hopfield energy. ICLR, 2023. paperJinsong Zhang, Qiang Fu, Xu Chen, Lun Du, Zelin Li, Gang Wang, xiaoguang Liu, Shi Han, and Dongmei Zhang.
Diversify and disambiguate: Out-of-distribution robustness via disagreement. ICLR, 2023. paperYoonho Lee, Huaxiu Yao, and Chelsea Finn.

RNNs

Variational LSTM enhanced anomaly detection for industrial big data. TII, 2021. paperXiaokang Zhou, Yiyong Hu, Wei Liang, Jianhua Ma, and Qun Jin.
Robust anomaly detection for multivariate time series through stochastic recurrent neural network. KDD, 2019. paperYa Su, Youjian Zhao, Chenhao Niu, Rong Liu, Wei Sun, and Dan Pei.
DeepLog: Anomaly detection and diagnosis from system logs through deep learning. CCS, 2017. paperMin Du, Feifei Li, Guineng Zheng, and Vivek Srikumar.
Unsupervised anomaly detection with LSTM neural networks. TNNLS, 2019. paperTolga Ergen and Suleyman Serdar Kozat.
LogAnomaly: Unsupervised detection of sequential and quantitative anomalies in unstructured logs. IJCAI, 2019. paperWeibin Meng, Ying Liu, Yichen Zhu, Shenglin Zhang, Dan Pei, Yuqing Liu, Yihao Chen, Ruizhi Zhang, Shimin Tao, Pei Sun, and Rong Zhou.
Outlier detection for time series with recurrent autoencoder ensembles. IJCAI, 2019. paperTung Kieu, Bin Yang, Chenjuan Guo, and Christian S. Jensen.
Learning regularity in skeleton trajectories for anomaly detection in videos. CVPR, 2019. paperRomero Morais, Vuong Le, Truyen Tran, Budhaditya Saha, Moussa Mansour, and Svetha Venkatesh.
LSTM-based encoder-decoder for multi-sensor anomaly detection. arXiv, 2016. paperPankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff.

Mechanism

Dataset

DoTA: Unsupervised detection of traffic anomaly in driving videos. TPAMI, 2022. paperYu Yao, Xizi Wang, Mingze Xu, Zelin Pu, Yuchen Wang, Ella Atkins, and David Crandall.
Revisiting time series outlier detection: Definitions and benchmarks. NIPS, 2021. paperKwei-Herng Lai, Daochen Zha, Junjie Xu, Yue Zhao, Guanchu Wang, and Xia Hu.
Street Scene: A new dataset and evaluation protocol for video anomaly detection. WACV, 2020. paperBharathkumar Ramachandra and Michael J. Jones.
The eyecandies dataset for unsupervised multimodal anomaly detection and localization. ACCV, 2020. paperLuca Bonfiglioli, Marco Toschi, Davide Silvestri, Nicola Fioraio, and Daniele De Gregorio.
Not only look, but also listen: Learning multimodal violence detection under weak supervision. ECCV, 2020. paperPeng Wu, Jing Liu, Yujia Shi, Yujia Sun, Fangtao Shao, Zhaoyang Wu, and Zhiwei Yang.
A revisit of sparse coding based anomaly detection in stacked RNN framework. ICCV, 2017. paperWeixin Luo, Wen Liu, and Shenghua Gao.
The MVTec anomaly detection dataset: A comprehensive real-world dataset for unsupervised anomaly detection. IJCV, 2021. paperPaul Bergmann, Kilian Batzner, Michael Fauser, David Sattlegger, and Carsten Steger.
Anomaly detection in crowded scenes. CVPR, 2010. paperVijay Mahadevan, Weixin Li, Viral Bhalodia, and Nuno Vasconcelos.
Abnormal event detection at 150 FPS in MATLAB. ICCV, 2013. paperCewu Lu, Jianping Shi, and Jiaya Jia.
Surface defect saliency of magnetic tile. The Visual Computer, 2020. paperYibin Huang, Congying Qiu, and Kui Yuan.

Library

ADBench: Anomaly detection benchmark. NIPS, 2022. paperSongqiao Han, Xiyang Hu, Hailiang Huang, Minqi Jiang, and Yue Zhao.
TSB-UAD: An end-to-end benchmark suite for univariate time-series anomaly detection. VLDB, 2022. paperJohn Paparrizos, Yuhao Kang, Paul Boniol, Ruey S. Tsay, Themis Palpanas, and Michael J. Franklin.
PyOD: A python toolbox for scalable outlier detection. JMLR, 2019. paperYue Zhao, Zain Nasrullah, and Zheng Li.
OpenOOD: Benchmarking generalized out-of-distribution detection. NIPS, 2022. paperJingkang Yang, Pengyun Wang, Dejian Zou, Zitang Zhou, Kunyuan Ding, Wenxuan Peng, Haoqi Wang, Guangyao Chen, Bo Li, Yiyou Sun, Xuefeng Du, Kaiyang Zhou, Wayne Zhang, Dan Hendrycks, Yixuan Li, and Ziwei Liu.
Towards a rigorous evaluation of rime-series anomaly detection. AAAI, 2022. paperSiwon Kim, Kukjin Choi, Hyun-Soo Choi, Byunghan Lee, and Sungroh Yoon.
Volume under the surface: A new accuracy evaluation measure for time-series anomaly detection. VLDB, 2022. paperJohn Paparrizos, Paul Boniol, Themis Palpanas, Ruey S. Tsa, Aaron Elmore, and Michael J. Franklin.
AnomalyKiTS: Anomaly detection toolkit for time series. AAAI, 2020. paperDhaval Patel, Giridhar Ganapavarapu, Srideepika Jayaraman, Shuxin Lin, Anuradha Bhamidipaty, and Jayant Kalagnanam.
TODS: An automated time series outlier detection system. AAAI, 2021. paperKwei-Herng Lai, Daochen Zha, Guanchu Wang, Junjie Xu, Yue Zhao, Devesh Kumar, Yile Chen, Purav Zumkhawaka, Minyang Wan, Diego Martinez, and Xia Hu.
BOND: Benchmarking unsupervised outlier node detection on static attributed graphs. NIPS, 2022. paperKay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, Lichao Sun, Jundong Li, George H. Chen, Zhihao Jia, and Philip S. Yu.
Ubnormal: New benchmark for supervised open-set video anomaly detection. CVPR, 2022. paperAndra Acsintoae, Andrei Florescu, Mariana-Iuliana Georgescu, Tudor Mare, Paul Sumedrea, Radu Tudor Ionescu, Fahad Shahbaz Khan, and Mubarak Shah.

Analysis

Are we certain it’s anomalous? arXiv, 2022. paperAlessandro Flaborea, Bardh Prenkaj, Bharti Munjal, Marco Aurelio Sterpa, Dario Aragona, Luca Podo, and Fabio Galasso.
Understanding anomaly detection with deep invertible networks through hierarchies of distributions and features. NIPS, 2020. paperRobin Schirrmeister, Yuxuan Zhou, Tonio Ball, and Dan Zhang.
Further analysis of outlier detection with deep generative models. NIPS, 2018. paperZiyu Wang, Bin Dai, David Wipf, and Jun Zhu.
Learning temporal regularity in video sequences. CVPR, 2016. paperMahmudul Hasan, Jonghyun Choi, Jan Neumann, Amit K. Roy-Chowdhury, and Larry S. Davis.
Local evaluation of time series anomaly detection algorithms. KDD, 2022. paperAlexis Huet, Jose Manuel Navarro, and Dario Rossi.
Adaptive model pooling for online deep anomaly detection from a complex evolving data stream. KDD, 2022. paperSusik Yoon, Youngjun Lee, Jae-Gil Lee, and Byung Suk Lee.
Anomaly detection in time series: A comprehensive evaluation. VLDB, 2022. paperSebastian Schmidl, Phillip Wenig, and Thorsten Papenbrock.
Anomaly detection requires better representations. arXiv, 2022. paperTal Reiss, Niv Cohen, Eliahu Horwitz, Ron Abutbul, and Yedid Hoshen.
Is it worth it? An experimental comparison of six deep and classical machine learning methods for unsupervised anomaly detection in time series. arXiv, 2022. paperFerdinand Rewicki, Joachim Denzler, and Julia Niebling.
FAPM: Fast adaptive patch memory for real-time industrial anomaly detection. arXiv, 2022. paperShinji Yamada, Satoshi Kamiya, and Kazuhiro Hotta.
Detecting data errors: Where are we and what needs to be done? VLDB, 2016. paperZiawasch Abedjan, Xu Chu, Dong Deng, Raul Castro Fernandez, Ihab F. Ilyas, Mourad Ouzzani, Paolo Papotti, Michael Stonebraker, and Nan Tang.
Data cleaning: Overview and emerging challenges. KDD, 2015. paperXu Chu, Ihab F. Ilyas, Sanjay Krishnan, and Jiannan Wang.
Video anomaly detection by solving decoupled spatio-temporal Jigsaw puzzles. ECCV, 2022. paperuodong Wang, Yunhong Wang, Jie Qin, Dongming Zhang, Xiuguo Bao, and Di Huang.
Learning causal temporal relation and feature discrimination for anomaly detection. TIP, 2021. paperPeng Wu and Jing Liu.
Unmasking the abnormal events in video. ICCV, 2017. paperRadu Tudor Ionescu, Sorina Smeureanu, Bogdan Alexe, and Marius Popescu.
Temporal cycle-consistency learning. CVPR, 2019. paperDebidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman.
Look at adjacent frames: Video anomaly detection without offline training. ECCV, 2022. paperYuqi Ouyang, Guodong Shen, and Victor Sanchez.
How to allocate your label budget? Choosing between active learning and learning to reject in anomaly detection. AAAI, 2023. paperLorenzo Perini, Daniele Giannuzzi, and Jesse Davis.
Deep anomaly detection under labeling budget constraints. arXiv, 2023. paperAodong Li, Chen Qiu, Padhraic Smyth, Marius Kloft, Stephan Mandt, and Maja Rudolph.
Diversity-measurable anomaly detection. arXiv, 2023. paperWenrui Liu, Hong Chang, Bingpeng Ma, Shiguang Shan, and Xilin Chen.
Transferring the contamination factor between anomaly detection domains by shape similarity. AAAI, 2022. paperLorenzo Perini, Vincent Vercruyssen, and Jesse Davis.

Domain Adaptation

Few-shot domain-adaptive anomaly detection for cross-site brain imagess. TPAMI, 2022. paperJianpo Su, Hui Shen, Limin Peng, and Dewen Hu.
Registration based few-shot anomaly detection. ECCV, 2021. paperChaoqin Huang, Haoyan Guan, Aofan Jiang, Ya Zhang, Michael Spratling, and Yanfeng Wang.
Learning unsupervised metaformer for anomaly detection. CVPR, 2021. paperJhih-Ciang Wu, Dingjie Chen, Chiou-Shann Fuh, and Tyng-Luh Liu.
Generic and scalable framework for automated time-series anomaly detection. KDD, 2019. paperNikolay Laptev, Saeed Amizadeh, and Ian Flint.
Transfer learning for anomaly detection through localized and unsupervised instance selection. AAAI, 2020. paperVincent Vercruyssen, Wannes Meert, and Jesse Davis.
FewSOME: Few shot anomaly detection. arXiv, 2023. paperNiamh Belton, Misgina Tsighe Hagos, Aonghus Lawlor, and Kathleen M. Curran.
Cross-domain video anomaly detection without target domain adaptation. WACV, 2023. paperAbhishek Aich, Kuanchuan Peng, and Amit K. Roy-Chowdhury.
Zero-shot anomaly detection without foundation models. arXiv, 2023. paperAodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, and Stephan Mandt.
Pushing the limits of fewshot anomaly detection in industry vision: A graphcore. ICLR, 2023. paperGuoyang Xie, Jinbao Wang, Jiaqi Liu, Yaochu Jin, and Feng Zheng.

Loss Function

Detecting regions of maximal divergence for spatio-temporal anomaly detection. TPAMI, 2018. paperBjörn Barz, Erik Rodner, Yanira Guanche Garcia, and Joachim Denzler.
Convex formulation for learning from positive and unlabeled data. ICML, 2015. paperMarthinus Christoffel Du Plessis, Gang Niu, and Masashi Sugiyama.

Lifelong Learning

PANDA: Adapting pretrained features for anomaly detection and segmentation. CVPR, 2021. paperTal Reiss, Niv Cohen, Liron Bergman, and Yedid Hoshen.
Continual learning for anomaly detection in surveillance videos. CVPR, 2020. paperKeval Doshi and Yasin Yilmaz.
Rethinking video anomaly detection-A continual learning approach. WACV, 2022. paperKeval Doshi and Yasin Yilmaz.
Continual learning for anomaly detection with variational autoencoder. ICASSP, 2019. paperFelix Wiewel and Bin Yang.
Lifelong anomaly detection through unlearning. CCS, 2019. paperMin Du, Zhi Chen, Chang Liu, Rajvardhan Oak, and Dawn Song.
xStream: Outlier detection in feature-evolving data streams. KDD, 2020. paperEmaad Manzoor, Hemank Lamba, and Leman Akoglu.
Continual learning approaches for anomaly detection. arXiv, 2022. paperDavide Dalle Pezze, Eugenia Anello, Chiara Masiero, and Gian Antonio Susto.
Towards lightweight, model-agnostic and diversity-aware active anomaly detection. ICLR, 2023. paperXu Zhang, Yuan Zhao, Ziang Cui, Liqun Li, Shilin He, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, and Dongmei Zhang.

Knowledge Distillation

Anomaly detection via reverse distillation from one-class embedding. CVPR, 2022. paperHanqiu Deng and Xingyu Li.
Multiresolution knowledge distillation for anomaly detection. CVPR, 2021. paperMohammadreza Salehi, Niousha Sadjadi, Soroosh Baselizadeh, Mohammad H. Rohban, and Hamid R. Rabiee.
Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings. CVPR, 2020. paperPaul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger.
Reconstructed student-teacher and discriminative networks for anomaly detection. IROS, 2022. paperShinji Yamada, Satoshi Kamiya, and Kazuhiro Hotta.
DeSTSeg: Segmentation guided denoising student-teacher for anomaly detection. arXiv, 2022. paperXuan Zhang, Shiyu Li, Xi Li, Ping Huang, Jiulong Shan, and Ting Chen.
Asymmetric student-teacher networks for industrial anomaly detection. WACV, 2023. paperMarco Rudolph, Tom Wehrbein, Bodo Rosenhahn, and Bastian Wandt.
In-painting radiography images for unsupervised anomaly detection. CVPR, 2023. paperTiange Xiang, Yongyi Lu, Alan L. Yuille, Chaoyi Zhang, Weidong Cai, and Zongwei Zhou.

Data Augmentation

Interpretable, multidimensional, multimodal anomaly detection with negative sampling for detection of device failure. ICML, 2020. paperJohn Sipple.
Doping: Generative data augmentation for unsupervised anomaly detection with GAN. ICDM, 2018. paperSwee Kiat Lim, Yi Loo, Ngoc-Trung Tran, Ngai-Man Cheung, Gemma Roig, and Yuval Elovici.
Detecting anomalies within time series using local neural transformations. arXiv, 2022. paperTim Schneider, Chen Qiu, Marius Kloft, Decky Aspandi Latif, Steffen Staab, Stephan Mandt, and Maja Rudolph.
Deep anomaly detection using geometric transformations. NIPS, 2018. paperIzhak Golan and Ran El-Yaniv.
Locally varying distance transform for unsupervised visual anomaly detection. ECCV, 2022. paperWenyan Lin, Zhonghang Liu, and Siying Liu.
DAGAD: Data augmentation for graph anomaly detection. ICDM, 2022. paperFanzhen Liu, Xiaoxiao Ma, Jia Wu, Jian Yang, Shan Xue†, Amin Beheshti, Chuan Zhou, Hao Peng, Quan Z. Sheng, and Charu C. Aggarwal.
Unsupervised dimension-contribution-aware embeddings transformation for anomaly detection. KBS, 2022. paperLiang Xi, Chenchen Liang, Han Liu, and Ao Li.
No shifted augmentations (NSA): Compact distributions for robust self-supervised Anomaly Detection. WACV, 2023. paperMohamed Yousef, Marcel Ackermann, Unmesh Kurup, and Tom Bishop.

Contrastive Learning

Graph anomaly detection via multi-scale contrastive learning networks with augmented view. AAAI, 2023. paperJingcan Duan, Siwei Wang, Pei Zhang, En Zhu, Jingtao Hu, Hu Jin, Yue Liu, and Zhibin Dong.
Partial and asymmetric contrastive learning for out-of-distribution detection in long-tailed recognition. ICML, 2022. paperHaotao Wang, Aston Zhang, Yi Zhu, Shuai Zheng, Mu Li, Alex Smola, and Zhangyang Wang.
Focus your distribution: Coarse-to-fine non-contrastive learning for anomaly detection and localization. ICME, 2022. paperYe Zheng, Xiang Wang, Rui Deng, Tianpeng Bao, Rui Zhao, and Liwei Wu.
MGFN: Magnitude-contrastive glance-and-focus network for weakly-supervised video anomaly detection. arXiv, 2023. paperYingxian Chen, Zhengzhe Liu, Baoheng Zhang, Wilton Fok, Xiaojuan Qi, and Yik-Chung Wu.
On the effectiveness of out-of-distribution data in self-supervised long-tail learning. ICLR, 2023. paperJianhong Bai, Zuozhu Liu, Hualiang Wang, Jin Hao, Yang Feng, Huanpeng Chu, and Haoji Hu.
Hierarchical semantic contrast for scene-aware video anomaly detection. CVPR, 2023. paperShengyang Sun and Xiaojin Gong.
Hierarchical semi-supervised contrastive learning for contamination-resistant anomaly detection. ECCV, 2022. paperGaoang Wang, Yibing Zhan, Xinchao Wang, Mingli Song, and Klara Nahrstedt.

Model Selection

Automatic unsupervised outlier model selection. NIPS, 2021. paperYue Zhao, Ryan Rossi, and Leman Akoglu.
Toward unsupervised outlier model selection. ICDM, 2022. paperYue Zhao, Sean Zhang, and Leman Akoglu.
Unsupervised model selection for time-series anomaly detection. ICLR, 2023. paperMononito Goswami, Cristian Ignacio Challu, Laurent Callot, Lenon Minorics, and Andrey Kan.

Gaussian Process

Deep anomaly detection with deviation networks. KDD, 2019. paperGuansong Pang, Chunhua Shen, and Anton van den Hengel.
Video anomaly detection and localization using hierarchical feature representation and Gaussian process regression. CVPR, 2015. paperKai-Wen Cheng and Yie-Tarng Chen, and Wen-Hsien Fang.
Multidimensional time series anomaly detection: A GRU-based Gaussian mixture variational autoencoder approach. ACCV, 2018. paperYifan Guo, Weixian Liao, Qianlong Wang, Lixing Yu, Tianxi Ji, and Pan Li.
Gaussian process regression-based video anomaly detection and localization with hierarchical feature representation. TIP, 2015. paperKaiwen Cheng, Yie-Tarng Chen, and Wen-Hsien Fang.

Multi Task

Beyond dents and scratches: Logical constraints in unsupervised anomaly detection and localization. IJCV, 2022. paperPaul Bergmann, Kilian Batzner, Michael Fauser, David Sattlegger, and Carsten Steger.
Anomaly detection in video via self-supervised and multi-task learning. CVPR, 2021. paperMariana-Iuliana Georgescu, Antonio Barbalau, Radu Tudor Ionescu, Fahad Shahbaz Khan, Marius Popescu, and Mubarak Shah.
Detecting semantic anomalies. AAAI, 2020. paperFaruk Ahmed and Aaron Courville.
MGADN: A multi-task graph anomaly detection network for multivariate time series. arXiv, 2022. paperWeixuan Xiong and Xiaochen Sun.

Outlier Exposure

Latent outlier exposure for anomaly detection with contaminated data. ICML, 2022. paperChen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, and Stephan Mandt.
Deep anomaly detection with outlier exposure. ICLR, 2019. paperDan Hendrycks, Mantas Mazeika, and Thomas Dietterich.
A simple and effective baseline for out-of-distribution detection using abstention. ICLR, 2021. paperSunil Thulasidasan, Sushil Thapa, Sayera Dhaubhadel, Gopinath Chennupati, Tanmoy Bhattacharya, and Jeff Bilmes.
Does your dermatology classifier know what it doesn’t know? Detecting the long-tail of unseen conditions. Medical Image Analysis, 2022. paperAbhijit Guha Roy, Jie Ren, Shekoofeh Azizi, Aaron Loh, Vivek Natarajan, Basil Mustafa, Nick Pawlowski, Jan Freyberg, Yuan Liu, Zach Beaver, Nam Vo, Peggy Bui, Samantha Winter, Patricia MacWilliams, Greg S. Corrado, Umesh Telang, Yun Liu, Taylan Cemgil, Alan Karthikesalingam, Balaji Lakshminarayanan, and Jim Winkens.

Statistics

(1+ε)-class classification: An anomaly detection method for highly imbalanced or incomplete data sets. JMLR, 2021. paperMaxim Borisyak, Artem Ryzhikov, Andrey Ustyuzhanin, Denis Derkach, Fedor Ratnikov, and Olga Mineeva.
Deep semi-supervised anomaly detection. ICLR, 2020. paperLukas Ruff, Robert A. Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, and Marius Kloft.
Online learning and sequential anomaly detection in trajectories. TPAMI, 2014. paperRikard Laxhammar and Göran Falkman.
COPOD: Copula-based outlier detection. ICDM, 2020. paperZheng Li, Yue Zhao, Nicola Botta, Cezar Ionescu, and Xiyang Hu.
ECOD: Unsupervised outlier detection using empirical cumulative distribution functions. TKDE, 2022. paperZheng Li, Yue Zhao, Xiyang Hu, Nicola Botta, Cezar Ionescu, and George Chen.
GLAD: A global-to-local anomaly detector. WACV, 2023. paperAitor Artola, Yannis Kolodziej, Jean-Michel Morel, and Thibaud Ehret.

Density Estimation

DenseHybrid: Hybrid anomaly detection for dense open-set recognition. ECCV, 2022. paperMatej Grcić, Petra Bevandić., and Siniša Šegvić.
Adaptive multi-stage density ratio estimation for learning latent space energy-based model. NIPS, 2022. paperZhisheng Xiao, and Tian Han.
Ultrafast local outlier detection from a data stream with stationary region skipping. KDD, 2020. paperSusik Yoon, Jae-Gil Lee, and Byung Suk Lee.
A discriminative framework for anomaly detection in large videos. ECCV, 2016. paperAllison Del Giorno, J. Andrew Bagnell, and Martial Hebert.
Hierarchical density estimates for data clustering, visualization, and outlier detection. ACM Transactions on Knowledge Discovery from Data, 2015. paperRicardo J. G. B. Campello, Davoud Moulavi, Arthur Zimek, and Jörg Sander.
Unsupervised anomaly detection by robust density estimation. AAAI, 2022. paperBoyang Liu, Pangning Tan, and Jiayu Zhou.

Memory Bank

Towards total recall in industrial anomaly detection. CVPR, 2022. paperKarsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, and Peter Gehler.
Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. ICCV, 2019. paperDong Gong, Lingqiao Liu, Vuong Le, Budhaditya Saha, Moussa Reda Mansour, Svetha Venkatesh, and Anton van den Hengel.

Active Learning

DADMoE: Anomaly detection with mixture-of-experts from noisy labels. AAAI, 2023. paperYue Zhao, Guoqing Zheng, Subhabrata Mukherjee, Robert McCann, and Ahmed Awadallah.
Incorporating expert feedback into active anomaly discovery. ICDM, 2016. paperShubhomoy Das, Weng-Keen Wong, Thomas Dietterich, Alan Fern, and Andrew Emmott.

Cluster

MIDAS: Microcluster-based detector of anomalies in edge streams. AAAI, 2020. paperSiddharth Bhatia, Bryan Hooi, Minji Yoon, Kijung Shin, and Christos Faloutsos.
Multiple dynamic outlier-detection from a data stream by exploiting duality of data and queries. SIGMOD, 2021. paperSusik Yoon, Yooju Shin, Jae-Gil Lee, and Byung Suk Lee.
Dynamic local aggregation network with adaptive clusterer for anomaly detection. ECCV, 2022. paperZhiwei Yang, Peng Wu, Jing Liu, and Xiaotao Liu.
Clustering and unsupervised anomaly detection with L2 normalized deep auto-encoder representations. IJCNN, 2018. paperCaglar Aytekin, Xingyang Ni, Francesco Cricri, and Emre Aksu.
Clustering driven deep autoencoder for video anomaly detection. ECCV, 2020. paperYunpeng Chang, Zhigang Tu, Wei Xie, and Junsong Yuan.

Isolation

Isolation distributional kernel: A new tool for kernel based anomaly detection. KDD, 2020. paperKai Ming Ting, Bicun Xu, Takashi Washio, and Zhihua Zhou.
AIDA: Analytic isolation and distance-based anomaly detection algorithm. arXiv, 2022. paperLuis Antonio Souto Arias, Cornelis W. Oosterlee, and Pasquale Cirillo.

Multimodal

Multimodal industrial anomaly detection via hybrid fusion. CVPR, 2023. paperYue Wang, Jinlong Peng, Jiangning Zhang, Ran Yi, Yabiao Wang, and Chengjie Wang.
A multimodal anomaly detector for robot-assisted feeding using an LSTM-based variational autoencoder. ICRA, 2018. paperDaehyung Park, Yuuna Hoshi, and Charles C. Kemp.

Energy Model

Deep structured energy based models for anomaly detection. ICML, 2016. paperShuangfei Zhai, Yu Cheng, Weining Lu, and Zhongfei Zhang.
Energy-based out-of-distribution detection. NIPS, 2020. paperWeitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li.
Learning neural set functions under the optimal subset oracle. NIPS, 2022. paperZijing Ou, Tingyang Xu, Qinliang Su, Yingzhen Li, Peilin Zhao, and Yatao Bian.

Application

Finance

Antibenford subgraphs: Unsupervised anomaly detection in financial networks. KDD, 2022. paperTianyi Chen and E. Tsourakakis.
Adversarial machine learning attacks against video anomaly detection systems. CVPR, 2022. paperFurkan Mumcu, Keval Doshi, and Yasin Yilmaz.

Point Cloud

Teacher-student network for 3D point cloud anomaly detection with few normal samples. arXiv, 2022. paperJianjian Qin, Chunzhi Gu, Jun Yu, and Chao Zhang.
Teacher-student network for 3D point cloud anomaly detection with few normal samples. WACV, 2023. paperPaul Bergmann and David Sattlegger.
Anomaly Detection in 3D Point Clouds Using Deep Geometric Descriptors. WACV, 2023. paperLokesh Veeramacheneni and Matias Valdenegro-Toro.

HPC

Anomaly detection using autoencoders in high performance computing systems. IAAI, 2019. paperAndrea Borghesi, Andrea Bartolini, Michele Lombardi, Michela Milano, and Luca Benini.

Intrusion

Intrusion detection using convolutional neural networks for representation learning. ICONIP, 2017. paperHipeng Li, Zheng Qin, Kai Huang, Xiao Yang, and Shuxiong Ye.

Diagnosis

Transformer-based normative modelling for anomaly detection of early schizophrenia. NIPS, 2022. paperPedro F Da Costa, Jessica Dafflon, Sergio Leonardo Mendes, João Ricardo Sato, M. Jorge Cardoso, Robert Leech, Emily JH Jones, and Walter H.L. Pinaya.

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