【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.

目录

Survey Papers

1. A survey of single-scene video anomaly detection. TPAMI, 2022. paper

   Bharathkumar Ramachandra, Michael J. Jones, and Ranga Raju Vatsavai.

2. Deep learning for anomaly detection: A review. ACM Computing Surveys, 2022. paper

   Guansong Pang, Chunhua Shen, Longbing Cao, and Anton Van Den Hengel.

3. A unifying review of deep and shallow anomaly detection. Proceedings of the IEEE, 2020. paper

   Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, GrÉgoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, and Klaus-robert MÜller.

4. A review on outlier/anomaly detection in time series data. ACM Computing Surveys, 2022. paper

   Ane Blázquez-García, Angel Conde, Usue Mori, and Jose A. Lozano.

5. Anomaly detection in autonomous driving: A survey. CVPR, 2022. paper

   Daniel Bogdoll, Maximilian Nitsche, and J. Marius Zöllner.

6. A comprehensive survey on graph anomaly detection with deep learning. TKDE, 2021. paper

   Xiaoxiao Ma, Jia Wu, Shan Xue, Jian Yang, Chuan Zhou, Quan Z. Sheng, and Hui Xiong, and Leman Akoglu.

7. Transformers in time series: A survey. arXiv, 2022. paper

   Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, and Liang Sun.

8. A survey on explainable anomaly detection. arXiv, 2022. paper

   Zhong Li, Yuxuan Zhu, and Matthijs van Leeuwen.

9. Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues. KBS, 2020. paper

   Arwa Aldweesh, Abdelouahid Derhab, and Ahmed Z.Emam.

10. Deep learning-based anomaly detection in cyber-physical systems: Progress and oportunities. ACM Computing Surveys, 2022. paper

    Yuan Luo, Ya Xiao, Long Cheng, Guojun Peng, and Danfeng (Daphne) Yao.

11. GAN-based anomaly detection: A review. Neurocomputing, 2022. paper

    Xuan Xia, Xizhou Pan, Nan Lia, Xing He, Lin Ma, Xiaoguang Zhang, and Ning Ding.

12. Unsupervised anomaly detection in time-series: An extensive evaluation and analysis of state-of-the-art methods. arXiv, 2022. paper

    Nesryne Mejri, Laura Lopez-Fuentes, Kankana Roy, Pavel Chernakov, Enjie Ghorbel, and Djamila Aouada.

13. Deep learning for time series anomaly detection: A survey. arXiv, 2022. paper

    Zahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan, Charu C. Aggarwal, and Mahsa Salehi.

14. A survey of deep learning-based network anomaly detection. Cluster Computing, 2019. paper

    Donghwoon Kwon, Hyunjoo Kim, Jinoh Kim, Sang C. Suh, Ikkyun Kim, and Kuinam J. Kim.

15. Survey on anomaly detection using data mining techniques. Procedia Computer Science, 2015. paper

    Shikha Agrawal and Jitendra Agrawal.

16. Graph based anomaly detection and description: A survey. Data Mining and Knowledge Discovery, 2015. paper

    Leman Akoglu, Hanghang Tong, and Danai Koutra.

17. Domain anomaly detection in machine perception: A system architecture and taxonomy. TPAMI, 2014. paper

    Josef Kittler, William Christmas, Teófilo de Campos, David Windridge, Fei Yan, John Illingworth, and Magda Osman.

18. Graph-based time-series anomaly detection: A Survey. arXiv, 2023. paper

    Thi Kieu Khanh Ho, Ali Karami, and Narges Armanfard.

19. Weakly supervised anomaly detection: A survey. arXiv, 2023. paper

    Minqi Jiang, Chaochuan Hou, Ao Zheng, Xiyang Hu, Songqiao Han, Hailiang Huang, Xiangnan He, Philip S. Yu, and Yue Zhao.

Methodology

AutoEncoder

1. Graph regularized autoencoder and its application in unsupervised anomaly detection. TPAMI, 2022. paper

   Imtiaz Ahmed, Travis Galoppo, Xia Hu, and Yu Ding.

2. Innovations autoencoder and its application in one-class anomalous sequence detection. JMLR, 2022. paper

   Xinyi Wang and Lang Tong.

3. Autoencoders-A comparative analysis in the realm of anomaly detection. CVPR, 2022. paper

   Sarah Schneider, Doris Antensteiner, Daniel Soukup, and Matthias Scheutz.

4. Attention guided anomaly localization in images. ECCV, 2020. paper

   Shashanka Venkataramanan, Kuan-Chuan Peng, Rajat Vikram Singh, and Abhijit Mahalanobis.

5. Latent space autoregression for novelty detection. CVPR, 2018. paper

   Davide Abati, Angelo Porrello, Simone Calderara, and Rita Cucchiara.

6. Anomaly detection in time series with robust variational quasi-recurrent autoencoders. ICDM, 2018. paper

   Tung Kieu, Bin Yang, Chenjuan Guo, Razvan-Gabriel Cirstea, Yan Zhao, Yale Song, and Christian S. Jensen.

7. Robust and explainable autoencoders for unsupervised time series outlier detection. ICDE, 2022. paper

   Tung Kieu, Bin Yang, Chenjuan Guo, Christian S. Jensen, Yan Zhao, Feiteng Huang, and Kai Zheng.

8. Latent feature learning via autoencoder training for automatic classification configuration recommendation. KBS, 2022. paper

   Liping Deng and MingQing Xiao.

9. Deep autoencoding Gaussian mixture model for unsupervised anomaly detection. ICLR, 2018. paper

   Bo Zongy, Qi Songz, Martin Renqiang Miny, Wei Chengy, Cristian Lumezanuy, Daeki Choy, and Haifeng Chen.

10. Anomaly detection with robust deep autoencoders. KDD, 2017. paper

    Chong Zhou and Randy C. Paffenroth.

11. Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications. WWW, 2018. paper

    Haowen 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.

12. Spatio-temporal autoencoder for video anomaly detection. MM, 2017. paper

    Yiru Zhao, Bing Deng, Chen Shen, Yao Liu, Hongtao Lu, and Xiansheng Hua.

13. Learning discriminative reconstructions for unsupervised outlier removal. ICCV, 2015. paper

    Yan Xia, Xudong Cao, Fang Wen, Gang Hua, and Jian Sun.

14. Outlier detection with autoencoder ensembles. ICDM, 2017. paper

    Jinghui Chen, Saket Sathey, Charu Aggarwaly, and Deepak Turaga.

15. A study of deep convolutional auto-encoders for anomaly detection in videos. Pattern Recognition Letters, 2018. paper

    Manassés Ribeiro, AndréEugênio Lazzaretti, and Heitor Silvério Lopes.

16. Classification-reconstruction learning for open-set recognition. CVPR, 2019. paper

    Ryota Yoshihashi, Shaodi You, Wen Shao, Makoto Iida, Rei Kawakami, and Takeshi Naemura.

17. Making reconstruction-based method great again for video anomaly detection. ICDM, 2022. paper

    Yizhou Wang, Can Qin, Yue Bai, Yi Xu, Xu Ma, and Yun Fu.

18. Two-stream decoder feature normality estimating network for industrial snomaly fetection. ICASSP, 2023. paper

    Chaewon Park, Minhyeok Lee, Suhwan Cho, Donghyeong Kim, and Sangyoun Lee.

19. Synthetic pseudo anomalies for unsupervised video anomaly detection: A simple yet efficient framework based on masked autoencoder. ICASSP, 2023. paper

    Xiangyu Huang, Caidan Zhao, Chenxing Gao, Lvdong Chen, and Zhiqiang Wu.

GAN

1. Stabilizing adversarially learned one-class novelty detection using pseudo anomalies. TIP, 2022. paper

   Muhammad Zaigham Zaheer, Jin-Ha Lee, Arif Mahmood, Marcella Astri, and Seung-Ik Lee.

2. GAN ensemble for anomaly detection. AAAI, 2021. paper

   Han, Xu, Xiaohui Chen, and Liping Liu.

3. Generative cooperative learning for unsupervised video anomaly detection. CVPR, 2022. paper

   Zaigham Zaheer, Arif Mahmood, M. Haris Khan, Mattia Segu, Fisher Yu, and Seung-Ik Lee.

4. GAN-based anomaly detection in imbalance problems. ECCV, 2020. paper

   Junbong Kim, Kwanghee Jeong, Hyomin Choi, and Kisung Seo.

5. Old is gold: Redefining the adversarially learned one-class classifier training paradigm. CVPR, 2020. paper

   Muhammad Zaigham Zaheer, Jin-ha Lee, Marcella Astrid, and Seung-Ik Lee.

6. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. IPMI, 2017. paper

   Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein, Ursula Schmidt-Erfurth, and Georg Langs.

7. Adversarially learned anomaly detection. ICDM, 2018. paper

   Houssam Zenati, Manon Romain, Chuan-Sheng Foo, Bruno Lecouat, and Vijay Chandrasekhar.

8. BeatGAN: Anomalous rhythm detection using adversarially generated time series. IJCAI, 2019. paper

   Bin Zhou, Shenghua Liu, Bryan Hooi, Xueqi Cheng, and Jing Ye.

9. Convolutional transformer based dual discriminator generative adversarial networks for video anomaly detection. MM, 2021. paper

   Xinyang Feng, Dongjin Song, Yuncong Chen, Zhengzhang Chen, Jingchao Ni, and Haifeng Chen.

10. USAD: Unsupervised anomaly detection on multivariate time series. KDD, 2020. paper

    Julien Audibert, Pietro Michiardi, Frédéric Guyard, Sébastien Marti, and Maria A. Zuluaga.

11. Anomaly detection with generative adversarial networks for multivariate time series. ICLR, 2018. paper

    Dan Li, Dacheng Chen, Jonathan Goh, and See-kiong Ng.

12. Efficient GAN-based anomaly detection. ICLR, 2018. paper

    Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, and Vijay Ramaseshan Chandrasekhar.

13. GANomaly: Semi-supervised anomaly detection via adversarial training. ACCV, 2019. paper

    Akcay, Samet, Amir Atapour-Abarghouei, and Toby P. Breckon.

14. f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis, 2019. paper

    Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein, Georg Langs, and Ursula Schmidt-Erfurth.

15. OCGAN: One-class novelty detection using GANs with constrained latent representations. CVPR, 2019. paper

    Pramuditha Perera, Ramesh Nallapati, and Bing Xiang.

16. Adversarially learned one-class classifier for novelty detection. CVPR, 2018. paper

    Mohammad Sabokrou, Mohammad Khalooei, Mahmood Fathy, and Ehsan Adeli.

17. Generative probabilistic novelty detection with adversarial autoencoders. NIPS, 2018. paper

    Stanislav Pidhorskyi, Ranya Almohsen, Donald A. Adjeroh, and Gianfranco Doretto.

18. Image anomaly detection with generative adversarial networks. ECML PKDD, 2018. paper

    Lucas Deecke, Robert Vandermeulen, Lukas Ruff, Stephan Mandt, and Marius Kloft.

19. RGI: Robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection. ICLR, 2023. paper

    Shancong Mou, Xiaoyi Gu, Meng Cao, Haoping Bai, Ping Huang, Jiulong Shan, and Jianjun Shi.

Flow

1. OneFlow: One-class flow for anomaly detection based on a minimal volume region. TPAMI, 2022. paper

   Lukasz Maziarka, Marek Smieja, Marcin Sendera, Lukasz Struski, Jacek Tabor, and Przemyslaw Spurek.

2. Comprehensive regularization in a bi-directional predictive network for video anomaly detection. AAAI, 2022. paper

   Chengwei Chen, Yuan Xie, Shaohui Lin, Angela Yao, Guannan Jiang, Wei Zhang, Yanyun Qu, Ruizhi Qiao, Bo Ren, and Lizhuang Ma.

3. Future frame prediction network for video anomaly detection. TPAMI, 2022. paper

   Weixin Luo, Wen Liu, Dongze Lian, and Shenghua Gao.

4. Graph-augmented normalizing flows for anomaly detection of multiple time series. ICLR, 2022. paper

   Enyan Dai and Jie Chen.

5. Cloze test helps: Effective video anomaly detection via learning to complete video events. MM, 2020. paper

   Guang Yu, Siqi Wang, Zhiping Cai, En Zhu, Chuanfu Xu, Jianping Yin, and Marius Kloft.

6. A modular and unified framework for detecting and localizing video anomalies. WACV, 2022. paper

   Keval Doshi and Yasin Yilmaz.

7. Video anomaly detection with compact feature sets for online performance. TIP, 2017. paper

   Roberto Leyva, Victor Sanchez, and Chang-Tsun Li.

8. U-Flow: A U-shaped normalizing flow for anomaly detection with unsupervised threshold. arXiv, 2017. paper

   Matías Tailanian, Álvaro Pardo, and Pablo Musé.

9. Bi-directional frame interpolation for unsupervised video anomaly detection. WACV, 2023. paper

   Hanqiu Deng, Zhaoxiang Zhang, Shihao Zou, and Xingyu Li.

10. AE-FLOW: Autoencoders with normalizing flows for medical images anomaly detection. ICLR, 2023. paper

    Yuzhong Zhao, Qiaoqiao Ding, and Xiaoqun Zhang.

11. A video anomaly detection framework based on appearance-motion semantics representation consistency. ICASSP, 2023. paper

    Xiangyu Huang, Caidan Zhao, and Zhiqiang Wu.

Diffusion Model

1. AnoDDPM: Anomaly detection with denoising diffusion probabilistic models using simplex noise. CVPR, 2022. paper

   Julian Wyatt, Adam Leach, Sebastian M. Schmon, and Chris G. Willcocks.

2. Diffusion models for medical anomaly detection. MICCAI, 2022. paper

   Julia Wolleb, Florentin Bieder, Robin Sandkühler, and Philippe C. Cattin.

3. DiffusionAD: Denoising diffusion for anomaly detection. arXiv, 2023. paper

   Hui Zhang, Zheng Wang, Zuxuan Wu, Yugang Jiang.

Transformer

1. Video anomaly detection via prediction network with enhanced spatio-temporal memory exchange. ICASSP, 2022. paper

   Guodong Shen, Yuqi Ouyang, and Victor Sanchez.

2. TranAD: Deep transformer networks for anomaly detection in multivariate time series data. VLDB, 2022. paper

   Shreshth Tuli, Giuliano Casale, and Nicholas R. Jennings.

3. Pixel-level anomaly detection via uncertainty-aware prototypical transformer. MM, 2022. paper

   Chao Huang, Chengliang Liu, Zheng Zhang, Zhihao Wu, Jie Wen, Qiuping Jiang, and Yong Xu.

4. AddGraph: Anomaly detection in dynamic graph using attention-based temporal GCN. IJCAI, 2019. paper

   Li Zheng, Zhenpeng Li, Jian Li, Zhao Li, and Jun Gao.

5. Anomaly transformer: Time series anomaly detection with association discrepancy. ICLR, 2022. paper

   Jiehui Xu, Haixu Wu, Jianmin Wang, and Mingsheng Long.

6. Constrained adaptive projection with pretrained features for anomaly detection. IJCAI, 2022. paper

   Xingtai Gui, Di Wu, Yang Chang, and Shicai Fan.

7. Self-training multi-sequence learning with transformer for weakly supervised video anomaly detection. AAAI, 2022. paper

   Shuo Li, Fang Liu, and Licheng Jiao.

8. Beyond outlier detection: Outlier interpretation by attention-guided triplet deviation network. WWW, 2021. paper

   Hongzuo Xu, Yijie Wang, Songlei Jian, Zhenyu Huang, Yongjun Wang, Ning Liu, and Fei Li.

9. Framing algorithmic recourse for anomaly detection. KDD, 2022. paper

   Debanjan Datta, Feng Chen, and Naren Ramakrishnan.

10. Inpainting transformer for anomaly detection. ICIAP, 2022. paper

    Jonathan Pirnay and Keng Chai.

11. Self-supervised and interpretable anomaly detection using network transformers. arXiv, 2022. paper

    Daniel L. Marino, Chathurika S. Wickramasinghe, Craig Rieger, and Milos Manic.

12. Anomaly detection in surveillance videos using transformer based attention model. arXiv, 2022. paper

    Kapil Deshpande, Narinder Singh Punn, Sanjay Kumar Sonbhadra, and Sonali Agarwal.

13. Multi-contextual predictions with vision transformer for video anomaly detection. arXiv, 2022. paper

    Joo-Yeon Lee, Woo-Jeoung Nam, and Seong-Whan Lee.

14. Transformer based models for unsupervised anomaly segmentation in brain MR images. arXiv, 2022. paper

    Ahmed Ghorbel, Ahmed Aldahdooh, Shadi Albarqouni, and Wassim Hamidouche.

15. HaloAE: An HaloNet based local transformer auto-encoder for anomaly detection and localization. arXiv, 2022. paper

    E. Mathian, H. Liu, L. Fernandez-Cuesta, D. Samaras, M. Foll, and L. Chen.

16. Generalizable industrial visual anomaly detection with self-induction vision transformer. arXiv, 2022. paper

    Haiming Yao and Xue Wang,.

17. VT-ADL: A vision transformer network for image anomaly detection and localization. ISIE, 2021. paper

    Pankaj Mishra, Riccardo Verk, Daniele Fornasier, Claudio Piciarelli, and Gian Luca Foresti.

Representation Learning

1. Localizing anomalies from weakly-labeled videos. TIP, 2021. paper

   Hui Lv, Chuanwei Zhou, Zhen Cui, Chunyan Xu, Yong Li, and Jian Yang.

2. PAC-Wrap: Semi-supervised PAC anomaly detection. KDD, 2022. paper

   Shuo Li, Xiayan Ji, Edgar Dobriban, Oleg Sokolsky, and Insup Lee.

3. Effective end-to-end unsupervised outlier detection via inlier priority of discriminative network. NIPS, 2019. paper

   Siqi Wang, Yijie Zeng, Xinwang Liu, En Zhu, Jianping Yin, Chuanfu Xu, and Marius Kloft.

4. AnomalyHop: An SSL-based image anomaly localization method. ICVCIP, 2021. paper

   Kaitai Zhang, Bin Wang, Wei Wang, Fahad Sohrab, Moncef Gabbouj, and C.-C. Jay Kuo.

5. Learning representations of ultrahigh-dimensional data for random distance-based outlier detection. KDD, 2018. paper

   Guansong Pang, Longbing Cao, Ling Chen, and Huan Liu.

6. Federated disentangled representation learning for unsupervised brain anomaly detection. NMI, 2022. paper

   Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert, and Shadi Albarqouni.

7. DSR–A dual subspace re-projection network for surface anomaly detection. ECCV, 2022. paper

   Vitjan Zavrtanik, Matej Kristan, and Danijel Skočaj.

8. LGN-Net: Local-global normality network for video anomaly detection. arXiv, 2022. paper

   Mengyang Zhao, Yang Liu, Jing Liu, Di Li, and Xinhua Zeng.

9. Glancing at the patch: Anomaly localization with global and local feature comparison. CVPR, 2021. paper

   Shenzhi Wang, Liwei Wu, Lei Cui, and Yujun Shen.

10. SPot-the-difference self-supervised pre-training for anomaly detection and segmentation. ECCV, 2022. paper

    Yang Zou, Jongheon Jeong, Latha Pemula, Dongqing Zhang, and Onkar Dabeer.

11. SSD: A unified framework for self-supervised outlier detection. ICLR, 2021. paper

    Vikash Sehwag, Mung Chiang, and Prateek Mittal.

12. NETS: Extremely fast outlier detection from a data stream via set-based processing. VLDB, 2019. paper

    Susik Yoon, Jae-Gil Lee, and Byung Suk Lee.

13. XGBOD: Improving supervised outlier detection with unsupervised representation learning. IJCNN, 2018. paper

    Yue Zhao and Maciej K. Hryniewicki.

14. Red PANDA: Disambiguating anomaly detection by removing nuisance factors. ICLR, 2023. paper

    Niv Cohen, Jonathan Kahana, and Yedid Hoshen.

15. TimesNet: Temporal 2D-variation modeling for general time series analysis. ICLR, 2023. paper

    Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long.

Nonparametric Approach

1. Real-time nonparametric anomaly detection in high-dimensional settings. TPAMI, 2021. paper

   Mehmet Necip Kurt, Yasin Yılmaz, and Xiaodong Wang.

2. Neighborhood structure assisted non-negative matrix factorization and its application in unsupervised point anomaly detection. JMLR, 2021. paper

   Imtiaz Ahmed, Xia Ben Hu, Mithun P. Acharya, and Yu Ding.

3. Bayesian nonparametric submodular video partition for robust anomaly detection. CVPR, 2022. paper

   Hitesh Sapkota and Qi Yu.

Reinforcement Learning

1. Towards experienced anomaly detector through reinforcement learning. AAAI, 2018. paper

   Chengqiang Huang, Yulei Wu, Yuan Zuo, Ke Pei, and Geyong Min.

2. Sequential anomaly detection using inverse reinforcement learning. KDD, 2019. paper

   Min-hwan Oh and Garud Iyengar.

3. Toward deep supervised anomaly detection: Reinforcement learning from partially labeled anomaly data. KDD, 2021. paper

   Guansong Pang, Anton van den Hengel, Chunhua Shen, and Longbing Cao.

4. Automated anomaly detection via curiosity-guided search and self-imitation learning. TNNLS, 2021. paper

   Yuening Li, Zhengzhang Chen, Daochen Zha, Kaixiong Zhou, Haifeng Jin, Haifeng Chen, and Xia Hu.

5. Meta-AAD: Active anomaly detection with deep reinforcement learning. ICDM, 2020. paper

   Daochen Zha, Kwei-Herng Lai, Mingyang Wan, and Xia Hu.

CNN

1. Self-supervised predictive convolutional attentive block for anomaly detection. CVPR, 2022. paper

   Nicolae-Catalin Ristea, Neelu Madan, Radu Tudor Ionescu, Kamal Nasrollahi, Fahad Shahbaz Khan, Thomas B. Moeslund, and Mubarak Shah.

2. Catching both gray and black swans: Open-set supervised anomaly detection. CVPR, 2022. paper

   Choubo Ding, Guansong Pang, and Chunhua Shen.

3. Learning memory-guided normality for anomaly detection. CVPR, 2020. paper

   Hyunjong Park, Jongyoun No, and Bumsub Ham.

4. CutPaste: Self-supervised learning for anomaly detection and localization. CVPR, 2021. paper

   Chunliang Li, Kihyuk Sohn, Jinsung Yoon, and Tomas Pfister.

5. Object-centric auto-encoders and dummy anomalies for abnormal event detection in video. CVPR, 2019. paper

   Radu Tudor Ionescu, Fahad Shahbaz Khan, Mariana-Iuliana Georgescu, and Ling Shao.

6. Mantra-Net: Manipulation tracing network for detection and localization of image forgeries with anomalous features. CVPR, 2019. paper

   Yue Wu, Wael AbdAlmageed, and Premkumar Natarajan.

7. Grad-CAM: Visual explanations from deep networks via gradient-based localization. ICCV, 2017. paper

   Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra.

8. A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. AAAI, 2019. paper

   Chuxu Zhang, Dongjin Song, Yuncong Chen, Xinyang Feng, Cristian Lumezanu, Wei Cheng, Jingchao Ni, Bo Zong, Haifeng Chen, and Nitesh V. Chawla.

9. Real-world anomaly detection in surveillance videos. CVPR, 2018. paper

   Waqas Sultani, Chen Chen, and Mubarak Shah.

10. FastAno: Fast anomaly detection via spatio-temporal patch transformation. WACV, 2022. paper

    Chaewon Park, MyeongAh Cho, Minhyeok Lee, and Sangyoun Lee.

11. Object class aware video anomaly detection through image translation. CRV, 2022. paper

    Mohammad Baradaran and Robert Bergevin.

12. Anomaly detection in video sequence with appearance-motion correspondence. ICCV, 2019. paper

    Trong-Nguyen Nguyen and Jean Meunier.

13. Joint detection and recounting of abnormal events by learning deep generic knowledge. ICCV, 2017. paper

    Ryota Hinami, Tao Mei, and Shin’ichi Satoh.

14. Deep-cascade: Cascading 3D deep neural networks for fast anomaly detection and localization in crowded scenes. TIP, 2017. paper

    Mohammad Sabokrou, Mohsen Fayyaz, Mahmood Fathy, and Reinhard Klette.

15. Towards interpretable video anomaly detection. WACV, 2023. paper

    Keval Doshi and Yasin Yilmaz.

16. Lossy compression for robust unsupervised time-series anomaly detection. CVPR, 2023. paper

    Christopher P. Ley and Jorge F. Silva.

17. Learning second order local anomaly for general face forgery detection. CVPR, 2022. paper

    Jianwei Fei, Yunshu Dai, Peipeng Yu, Tianrun Shen, Zhihua Xia, and Jian Weng.

Graph Neural Network

1. Graph convolutional label noise cleaner: Train a plug-and-play action classifier for anomaly detection. CVPR, 2019. paper

   Jiaxing Zhong, Nannan Li, Weijie Kong, Shan Liu, Thomas H. Li, and Ge Li.

2. Towards open set video anomaly detection. ECCV, 2019. paper

   Yuansheng Zhu, Wentao Bao, and Qi Yu.

3. Decoupling representation learning and classification for GNN-based anomaly detection. SIGIR, 2021. paper

   Yanling Wan,, Jing Zhang, Shasha Guo, Hongzhi Yin, Cuiping Li, and Hong Chen.

4. Crowd-level abnormal behavior detection via multi-scale motion consistency learning. AAAI, 2023. paper

   Linbo Luo, Yuanjing Li, Haiyan Yin, Shangwei Xie, Ruimin Hu, and Wentong Cai.

5. Rethinking graph neural networks for anomaly detection. ICML, 2022. paper

   Jianheng Tang, Jiajin Li, Ziqi Gao, and Jia Li.

6. Cross-domain graph anomaly detection via anomaly-aware contrastive alignment. AAAI, 2023. paper

   Qizhou Wang, Guansong Pang, Mahsa Salehi, Wray Buntine, and Christopher Leckie.

7. A causal inference look at unsupervised video anomaly detection. AAAI, 2022. paper

   Xiangru Lin, Yuyang Chen, Guanbin Li, and Yizhou Yu.

8. NetWalk: A flexible deep embedding approach for anomaly detection in dynamic networks. KDD, 2018. paper

   Wenchao Yu, Wei Cheng, Charu C. Aggarwal, Kai Zhang, Haifeng Chen, and Wei Wang.

9. LUNAR: Unifying local outlier detection methods via graph neural networks. AAAI, 2022. paper

   Adam Goodge, Bryan Hooi, See-Kiong Ng, and Wee Siong Ng.

10. Series2Graph: Graph-based subsequence anomaly detection for time series. VLDB, 2022. paper

    Paul Boniol and Themis Palpanas.

11. Graph embedded pose clustering for anomaly detection. CVPR, 2020. paper

    Amir Markovitz, Gilad Sharir, Itamar Friedman, Lihi Zelnik-Manor, and Shai Avidan.

12. Fast memory-efficient anomaly detection in streaming heterogeneous graphs. KDD, 2016. paper

    Emaad Manzoor, Sadegh M. Milajerdi, and Leman Akoglu.

13. Raising the bar in graph-level anomaly detection. IJCAI, 2022. paper

    Chen Qiu, Marius Kloft, Stephan Mandt, and Maja Rudolph.

14. SpotLight: Detecting anomalies in streaming graphs. KDD, 2018. paper

    Dhivya Eswaran, Christos Faloutsos, Sudipto Guha, and Nina Mishra.

15. Graph anomaly detection via multi-scale contrastive learning networks with augmented view. AAAI, 2023. paper

    Jingcan Duan, Siwei Wang, Pei Zhang, En Zhu, Jingtao Hu, Hu Jin, Yue Liu, and Zhibin Dong.

16. Counterfactual graph learning for anomaly detection on attributed networks. TKDE, 2023. paper

    Chunjing Xiao, Xovee Xu, Yue Lei, Kunpeng Zhang, Siyuan Liu, and Fan Zhou.

17. Deep variational graph convolutional recurrent network for multivariate time series anomaly detection. ICML, 2022. paper

    Wenchao Chen, Long Tian, Bo Chen, Liang Dai, Zhibin Duan, and Mingyuan Zhou.

Sparse Coding

1. Video anomaly detection with sparse coding inspired deep neural networks. TPAMI, 2021. paper

   Weixin Luo, Wen Liu, Dongze Lian, Jinhui Tang, Lixin Duan, Xi Peng, and Shenghua Gao.

2. Self-supervised sparse representation for video anomaly detection. ECCV, 2022. paper

   Jhihciang Wu, Heyen Hsieh, Dingjie Chen, Chioushann Fuh, and Tyngluh Liu.

3. A revisit of sparse coding based anomaly detection in stacked RNN framework. ICCV, 2017. paper

   Weixin Luo, Wen Liu, and Shenghua Gao.

4. HashNWalk: Hash and random walk based anomaly detection in hyperedge streams. IJCAI, 2022. paper

   Geon Lee, Minyoung Choe, and Kijung Shin.

5. Fast abnormal event detection. IJCV, 2019. paper

   Cewu Lu, Jianping Shi, Weiming Wang, and Jiaya Jia.

Support Vector

1. Patch SVDD: Patch-level SVDD for anomaly detection and segmentation. ACCV, 2020. paper

   Jihun Yi and Sungroh Yoon.

2. Multiclass anomaly detector: The CS++ support vector machine. JMLR, 2020. paper

   Alistair Shilton, Sutharshan Rajasegarar, and Marimuthu Palaniswami.

3. Timeseries anomaly detection using temporal hierarchical one-class network. NIPS, 2020. paper

   Lifeng Shen, Zhuocong Li, and James Kwok.

4. LOSDD: Leave-out support vector data description for outlier detection. arXiv, 2022. paper

   Daniel Boiar, Thomas Liebig, and Erich Schubert.

5. Anomaly detection using one-class neural networks. arXiv, 2018. paper

   Raghavendra Chalapathy, Aditya Krishna Menon, and Sanjay Chawla.

6. Deep graph stream SVDD: Anomaly detection in cyber-physical systems. PAKDD, 2023. paper

   Ehtesamul Azim, Dongjie Wang, and Yanjie Fu.

OOD

1. Your out-of-distribution detection method is not robust! NIPS, 2022. paper

   Mohammad Azizmalayeri, Arshia Soltani Moakhar, Arman Zarei, Reihaneh Zohrabi, Mohammad Taghi Manzuri, and Mohammad Hossein Rohban.

2. Exploiting mixed unlabeled data for detecting samples of seen and unseen out-of-distribution classes. AAAI, 2022. paper

   Yixuan Sun and Wei Wang.

3. RankFeat: Rank-1 feature removal for out-of-distribution detection. AAAI, 2022. paper

   Yue Song, Nicu Sebe, and Wei Wang.

4. Detect, distill and update: Learned DB systems facing out of distribution data. SIGMOD, 2023. paper

   Meghdad Kurmanji and Peter Triantafillou.

5. Beyond mahalanobis distance for textual OOD detection. NIPS, 2022. paper

   Pierre Colombo, Eduardo Dadalto Câmara Gomes, Guillaume Staerman, Nathan Noiry, and Pablo Piantanida.

6. Exploring the limits of out-of-distribution detection. NIPS, 2021. paper

   Stanislav Fort, Jie Ren, and Balaji Lakshminarayanan.

7. Is out-of-distribution detection learnable? ICLR, 2022. paper

   Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, and Feng Liu.

8. Out-of-distribution detection is not all you need. NIPS, 2022. paper

   Joris Guerin, Kevin Delmas, Raul Sena Ferreira, and Jérémie Guiochet.

9. iDECODe: In-distribution equivariance for conformal out-of-distribution detection. AAAI, 2022. paper

   Ramneet Kaur, Susmit Jha, Anirban Roy, Sangdon Park, Edgar Dobriban, Oleg Sokolsky, and Insup Lee.

10. Out-of-distribution detection using an ensemble of self supervised leave-out classifiers. ECCV, 2018. paper

    Apoorv Vyas, Nataraj Jammalamadaka, Xia Zhu, Dipankar Das, Bharat Kaul, and Theodore L. Willke.

11. Self-supervised learning for generalizable out-of-distribution detection. AAAI, 2020. paper

    Sina Mohseni, Mandar Pitale, JBS Yadawa, and Zhangyang Wang.

12. Augmenting softmax information for selective classification with out-of-distribution data. ACCV, 2022. paper

    Guoxuan Xia and Christos-Savvas Bouganis.

13. Robustness to spurious correlations improves semantic out-of-distribution detection. AAAI, 2023. paper

    Lily H. Zhang and Rajesh Ranganath.

14. Non-parametric outlier synthesis. ICLR, 2023. paper

    Leitian Tao, Xuefeng Du, Jerry Zhu, and Yixuan Li.

15. Out-of-distribution detection with implicit outlier transformation. ICLR, 2023. paper

    Qizhou Wang, Junjie Ye, Feng Liu, Quanyu Dai, Marcus Kalander, Tongliang Liu, Jianye Hao, and Bo Han.

16. Out-of-distribution representation learning for time series classification. ICLR, 2023. paper

    Wang Lu, Jindong Wang, Xinwei Sun, Yiqiang Chen, and Xing Xie.

17. Out-of-distribution detection based on in-distribution data patterns memorization with modern Hopfield energy. ICLR, 2023. paper

    Jinsong Zhang, Qiang Fu, Xu Chen, Lun Du, Zelin Li, Gang Wang, xiaoguang Liu, Shi Han, and Dongmei Zhang.

18. Diversify and disambiguate: Out-of-distribution robustness via disagreement. ICLR, 2023. paper

    Yoonho Lee, Huaxiu Yao, and Chelsea Finn.

RNNs

1. Variational LSTM enhanced anomaly detection for industrial big data. TII, 2021. paper

   Xiaokang Zhou, Yiyong Hu, Wei Liang, Jianhua Ma, and Qun Jin.

2. Robust anomaly detection for multivariate time series through stochastic recurrent neural network. KDD, 2019. paper

   Ya Su, Youjian Zhao, Chenhao Niu, Rong Liu, Wei Sun, and Dan Pei.

3. DeepLog: Anomaly detection and diagnosis from system logs through deep learning. CCS, 2017. paper

   Min Du, Feifei Li, Guineng Zheng, and Vivek Srikumar.

4. Unsupervised anomaly detection with LSTM neural networks. TNNLS, 2019. paper

   Tolga Ergen and Suleyman Serdar Kozat.

5. LogAnomaly: Unsupervised detection of sequential and quantitative anomalies in unstructured logs. IJCAI, 2019. paper

   Weibin Meng, Ying Liu, Yichen Zhu, Shenglin Zhang, Dan Pei, Yuqing Liu, Yihao Chen, Ruizhi Zhang, Shimin Tao, Pei Sun, and Rong Zhou.

6. Outlier detection for time series with recurrent autoencoder ensembles. IJCAI, 2019. paper

   Tung Kieu, Bin Yang, Chenjuan Guo, and Christian S. Jensen.

7. Learning regularity in skeleton trajectories for anomaly detection in videos. CVPR, 2019. paper

   Romero Morais, Vuong Le, Truyen Tran, Budhaditya Saha, Moussa Mansour, and Svetha Venkatesh.

8. LSTM-based encoder-decoder for multi-sensor anomaly detection. arXiv, 2016. paper

   Pankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff.

Mechanism

Dataset

1. DoTA: Unsupervised detection of traffic anomaly in driving videos. TPAMI, 2022. paper

   Yu Yao, Xizi Wang, Mingze Xu, Zelin Pu, Yuchen Wang, Ella Atkins, and David Crandall.

2. Revisiting time series outlier detection: Definitions and benchmarks. NIPS, 2021. paper

   Kwei-Herng Lai, Daochen Zha, Junjie Xu, Yue Zhao, Guanchu Wang, and Xia Hu.

3. Street Scene: A new dataset and evaluation protocol for video anomaly detection. WACV, 2020. paper

   Bharathkumar Ramachandra and Michael J. Jones.

4. The eyecandies dataset for unsupervised multimodal anomaly detection and localization. ACCV, 2020. paper

   Luca Bonfiglioli, Marco Toschi, Davide Silvestri, Nicola Fioraio, and Daniele De Gregorio.

5. Not only look, but also listen: Learning multimodal violence detection under weak supervision. ECCV, 2020. paper

   Peng Wu, Jing Liu, Yujia Shi, Yujia Sun, Fangtao Shao, Zhaoyang Wu, and Zhiwei Yang.

6. A revisit of sparse coding based anomaly detection in stacked RNN framework. ICCV, 2017. paper

   Weixin Luo, Wen Liu, and Shenghua Gao.

7. The MVTec anomaly detection dataset: A comprehensive real-world dataset for unsupervised anomaly detection. IJCV, 2021. paper

   Paul Bergmann, Kilian Batzner, Michael Fauser, David Sattlegger, and Carsten Steger.

8. Anomaly detection in crowded scenes. CVPR, 2010. paper

   Vijay Mahadevan, Weixin Li, Viral Bhalodia, and Nuno Vasconcelos.

9. Abnormal event detection at 150 FPS in MATLAB. ICCV, 2013. paper

   Cewu Lu, Jianping Shi, and Jiaya Jia.

10. Surface defect saliency of magnetic tile. The Visual Computer, 2020. paper

    Yibin Huang, Congying Qiu, and Kui Yuan.

Library

1. ADBench: Anomaly detection benchmark. NIPS, 2022. paper

   Songqiao Han, Xiyang Hu, Hailiang Huang, Minqi Jiang, and Yue Zhao.

2. TSB-UAD: An end-to-end benchmark suite for univariate time-series anomaly detection. VLDB, 2022. paper

   John Paparrizos, Yuhao Kang, Paul Boniol, Ruey S. Tsay, Themis Palpanas, and Michael J. Franklin.

3. PyOD: A python toolbox for scalable outlier detection. JMLR, 2019. paper

   Yue Zhao, Zain Nasrullah, and Zheng Li.

4. OpenOOD: Benchmarking generalized out-of-distribution detection. NIPS, 2022. paper

   Jingkang 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.

5. Towards a rigorous evaluation of rime-series anomaly detection. AAAI, 2022. paper

   Siwon Kim, Kukjin Choi, Hyun-Soo Choi, Byunghan Lee, and Sungroh Yoon.

6. Volume under the surface: A new accuracy evaluation measure for time-series anomaly detection. VLDB, 2022. paper

   John Paparrizos, Paul Boniol, Themis Palpanas, Ruey S. Tsa, Aaron Elmore, and Michael J. Franklin.

7. AnomalyKiTS: Anomaly detection toolkit for time series. AAAI, 2020. paper

   Dhaval Patel, Giridhar Ganapavarapu, Srideepika Jayaraman, Shuxin Lin, Anuradha Bhamidipaty, and Jayant Kalagnanam.

8. TODS: An automated time series outlier detection system. AAAI, 2021. paper

   Kwei-Herng Lai, Daochen Zha, Guanchu Wang, Junjie Xu, Yue Zhao, Devesh Kumar, Yile Chen, Purav Zumkhawaka, Minyang Wan, Diego Martinez, and Xia Hu.

9. BOND: Benchmarking unsupervised outlier node detection on static attributed graphs. NIPS, 2022. paper

   Kay 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.

10. Ubnormal: New benchmark for supervised open-set video anomaly detection. CVPR, 2022. paper

    Andra Acsintoae, Andrei Florescu, Mariana-Iuliana Georgescu, Tudor Mare, Paul Sumedrea, Radu Tudor Ionescu, Fahad Shahbaz Khan, and Mubarak Shah.

Analysis

1. Are we certain it’s anomalous? arXiv, 2022. paper

   Alessandro Flaborea, Bardh Prenkaj, Bharti Munjal, Marco Aurelio Sterpa, Dario Aragona, Luca Podo, and Fabio Galasso.

2. Understanding anomaly detection with deep invertible networks through hierarchies of distributions and features. NIPS, 2020. paper

   Robin Schirrmeister, Yuxuan Zhou, Tonio Ball, and Dan Zhang.

3. Further analysis of outlier detection with deep generative models. NIPS, 2018. paper

   Ziyu Wang, Bin Dai, David Wipf, and Jun Zhu.

4. Learning temporal regularity in video sequences. CVPR, 2016. paper

   Mahmudul Hasan, Jonghyun Choi, Jan Neumann, Amit K. Roy-Chowdhury, and Larry S. Davis.

5. Local evaluation of time series anomaly detection algorithms. KDD, 2022. paper

   Alexis Huet, Jose Manuel Navarro, and Dario Rossi.

6. Adaptive model pooling for online deep anomaly detection from a complex evolving data stream. KDD, 2022. paper

   Susik Yoon, Youngjun Lee, Jae-Gil Lee, and Byung Suk Lee.

7. Anomaly detection in time series: A comprehensive evaluation. VLDB, 2022. paper

   Sebastian Schmidl, Phillip Wenig, and Thorsten Papenbrock.

8. Anomaly detection requires better representations. arXiv, 2022. paper

   Tal Reiss, Niv Cohen, Eliahu Horwitz, Ron Abutbul, and Yedid Hoshen.

9. Is it worth it? An experimental comparison of six deep and classical machine learning methods for unsupervised anomaly detection in time series. arXiv, 2022. paper

   Ferdinand Rewicki, Joachim Denzler, and Julia Niebling.

10. FAPM: Fast adaptive patch memory for real-time industrial anomaly detection. arXiv, 2022. paper

    Shinji Yamada, Satoshi Kamiya, and Kazuhiro Hotta.

11. Detecting data errors: Where are we and what needs to be done? VLDB, 2016. paper

    Ziawasch Abedjan, Xu Chu, Dong Deng, Raul Castro Fernandez, Ihab F. Ilyas, Mourad Ouzzani, Paolo Papotti, Michael Stonebraker, and Nan Tang.

12. Data cleaning: Overview and emerging challenges. KDD, 2015. paper

    Xu Chu, Ihab F. Ilyas, Sanjay Krishnan, and Jiannan Wang.

13. Video anomaly detection by solving decoupled spatio-temporal Jigsaw puzzles. ECCV, 2022. paper

    uodong Wang, Yunhong Wang, Jie Qin, Dongming Zhang, Xiuguo Bao, and Di Huang.

14. Learning causal temporal relation and feature discrimination for anomaly detection. TIP, 2021. paper

    Peng Wu and Jing Liu.

15. Unmasking the abnormal events in video. ICCV, 2017. paper

    Radu Tudor Ionescu, Sorina Smeureanu, Bogdan Alexe, and Marius Popescu.

16. Temporal cycle-consistency learning. CVPR, 2019. paper

    Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman.

17. Look at adjacent frames: Video anomaly detection without offline training. ECCV, 2022. paper

    Yuqi Ouyang, Guodong Shen, and Victor Sanchez.

18. How to allocate your label budget? Choosing between active learning and learning to reject in anomaly detection. AAAI, 2023. paper

    Lorenzo Perini, Daniele Giannuzzi, and Jesse Davis.

19. Deep anomaly detection under labeling budget constraints. arXiv, 2023. paper

    Aodong Li, Chen Qiu, Padhraic Smyth, Marius Kloft, Stephan Mandt, and Maja Rudolph.

20. Diversity-measurable anomaly detection. arXiv, 2023. paper

    Wenrui Liu, Hong Chang, Bingpeng Ma, Shiguang Shan, and Xilin Chen.

21. Transferring the contamination factor between anomaly detection domains by shape similarity. AAAI, 2022. paper

    Lorenzo Perini, Vincent Vercruyssen, and Jesse Davis.

Domain Adaptation

1. Few-shot domain-adaptive anomaly detection for cross-site brain imagess. TPAMI, 2022. paper

   Jianpo Su, Hui Shen, Limin Peng, and Dewen Hu.

2. Registration based few-shot anomaly detection. ECCV, 2021. paper

   Chaoqin Huang, Haoyan Guan, Aofan Jiang, Ya Zhang, Michael Spratling, and Yanfeng Wang.

3. Learning unsupervised metaformer for anomaly detection. CVPR, 2021. paper

   Jhih-Ciang Wu, Dingjie Chen, Chiou-Shann Fuh, and Tyng-Luh Liu.

4. Generic and scalable framework for automated time-series anomaly detection. KDD, 2019. paper

   Nikolay Laptev, Saeed Amizadeh, and Ian Flint.

5. Transfer learning for anomaly detection through localized and unsupervised instance selection. AAAI, 2020. paper

   Vincent Vercruyssen, Wannes Meert, and Jesse Davis.

6. FewSOME: Few shot anomaly detection. arXiv, 2023. paper

   Niamh Belton, Misgina Tsighe Hagos, Aonghus Lawlor, and Kathleen M. Curran.

7. Cross-domain video anomaly detection without target domain adaptation. WACV, 2023. paper

   Abhishek Aich, Kuanchuan Peng, and Amit K. Roy-Chowdhury.

8. Zero-shot anomaly detection without foundation models. arXiv, 2023. paper

   Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, and Stephan Mandt.

9. Pushing the limits of fewshot anomaly detection in industry vision: A graphcore. ICLR, 2023. paper

   Guoyang Xie, Jinbao Wang, Jiaqi Liu, Yaochu Jin, and Feng Zheng.

Loss Function

1. Detecting regions of maximal divergence for spatio-temporal anomaly detection. TPAMI, 2018. paper

   Björn Barz, Erik Rodner, Yanira Guanche Garcia, and Joachim Denzler.

2. Convex formulation for learning from positive and unlabeled data. ICML, 2015. paper

   Marthinus Christoffel Du Plessis, Gang Niu, and Masashi Sugiyama.

Lifelong Learning

1. PANDA: Adapting pretrained features for anomaly detection and segmentation. CVPR, 2021. paper

   Tal Reiss, Niv Cohen, Liron Bergman, and Yedid Hoshen.

2. Continual learning for anomaly detection in surveillance videos. CVPR, 2020. paper

   Keval Doshi and Yasin Yilmaz.

3. Rethinking video anomaly detection-A continual learning approach. WACV, 2022. paper

   Keval Doshi and Yasin Yilmaz.

4. Continual learning for anomaly detection with variational autoencoder. ICASSP, 2019. paper

   Felix Wiewel and Bin Yang.

5. Lifelong anomaly detection through unlearning. CCS, 2019. paper

   Min Du, Zhi Chen, Chang Liu, Rajvardhan Oak, and Dawn Song.

6. xStream: Outlier detection in feature-evolving data streams. KDD, 2020. paper

   Emaad Manzoor, Hemank Lamba, and Leman Akoglu.

7. Continual learning approaches for anomaly detection. arXiv, 2022. paper

   Davide Dalle Pezze, Eugenia Anello, Chiara Masiero, and Gian Antonio Susto.

8. Towards lightweight, model-agnostic and diversity-aware active anomaly detection. ICLR, 2023. paper

   Xu Zhang, Yuan Zhao, Ziang Cui, Liqun Li, Shilin He, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, and Dongmei Zhang.

Knowledge Distillation

1. Anomaly detection via reverse distillation from one-class embedding. CVPR, 2022. paper

   Hanqiu Deng and Xingyu Li.

2. Multiresolution knowledge distillation for anomaly detection. CVPR, 2021. paper

   Mohammadreza Salehi, Niousha Sadjadi, Soroosh Baselizadeh, Mohammad H. Rohban, and Hamid R. Rabiee.

3. Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings. CVPR, 2020. paper

   Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger.

4. Reconstructed student-teacher and discriminative networks for anomaly detection. IROS, 2022. paper

   Shinji Yamada, Satoshi Kamiya, and Kazuhiro Hotta.

5. DeSTSeg: Segmentation guided denoising student-teacher for anomaly detection. arXiv, 2022. paper

   Xuan Zhang, Shiyu Li, Xi Li, Ping Huang, Jiulong Shan, and Ting Chen.

6. Asymmetric student-teacher networks for industrial anomaly detection. WACV, 2023. paper

   Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn, and Bastian Wandt.

7. In-painting radiography images for unsupervised anomaly detection. CVPR, 2023. paper

   Tiange Xiang, Yongyi Lu, Alan L. Yuille, Chaoyi Zhang, Weidong Cai, and Zongwei Zhou.

Data Augmentation

1. Interpretable, multidimensional, multimodal anomaly detection with negative sampling for detection of device failure. ICML, 2020. paper

   John Sipple.

2. Doping: Generative data augmentation for unsupervised anomaly detection with GAN. ICDM, 2018. paper

   Swee Kiat Lim, Yi Loo, Ngoc-Trung Tran, Ngai-Man Cheung, Gemma Roig, and Yuval Elovici.

3. Detecting anomalies within time series using local neural transformations. arXiv, 2022. paper

   Tim Schneider, Chen Qiu, Marius Kloft, Decky Aspandi Latif, Steffen Staab, Stephan Mandt, and Maja Rudolph.

4. Deep anomaly detection using geometric transformations. NIPS, 2018. paper

   Izhak Golan and Ran El-Yaniv.

5. Locally varying distance transform for unsupervised visual anomaly detection. ECCV, 2022. paper

   Wenyan Lin, Zhonghang Liu, and Siying Liu.

6. DAGAD: Data augmentation for graph anomaly detection. ICDM, 2022. paper

   Fanzhen Liu, Xiaoxiao Ma, Jia Wu, Jian Yang, Shan Xue†, Amin Beheshti, Chuan Zhou, Hao Peng, Quan Z. Sheng, and Charu C. Aggarwal.

7. Unsupervised dimension-contribution-aware embeddings transformation for anomaly detection. KBS, 2022. paper

   Liang Xi, Chenchen Liang, Han Liu, and Ao Li.

8. No shifted augmentations (NSA): Compact distributions for robust self-supervised Anomaly Detection. WACV, 2023. paper

   Mohamed Yousef, Marcel Ackermann, Unmesh Kurup, and Tom Bishop.

Contrastive Learning

1. Graph anomaly detection via multi-scale contrastive learning networks with augmented view. AAAI, 2023. paper

   Jingcan Duan, Siwei Wang, Pei Zhang, En Zhu, Jingtao Hu, Hu Jin, Yue Liu, and Zhibin Dong.

2. Partial and asymmetric contrastive learning for out-of-distribution detection in long-tailed recognition. ICML, 2022. paper

   Haotao Wang, Aston Zhang, Yi Zhu, Shuai Zheng, Mu Li, Alex Smola, and Zhangyang Wang.

3. Focus your distribution: Coarse-to-fine non-contrastive learning for anomaly detection and localization. ICME, 2022. paper

   Ye Zheng, Xiang Wang, Rui Deng, Tianpeng Bao, Rui Zhao, and Liwei Wu.

4. MGFN: Magnitude-contrastive glance-and-focus network for weakly-supervised video anomaly detection. arXiv, 2023. paper

   Yingxian Chen, Zhengzhe Liu, Baoheng Zhang, Wilton Fok, Xiaojuan Qi, and Yik-Chung Wu.

5. On the effectiveness of out-of-distribution data in self-supervised long-tail learning. ICLR, 2023. paper

   Jianhong Bai, Zuozhu Liu, Hualiang Wang, Jin Hao, Yang Feng, Huanpeng Chu, and Haoji Hu.

6. Hierarchical semantic contrast for scene-aware video anomaly detection. CVPR, 2023. paper

   Shengyang Sun and Xiaojin Gong.

7. Hierarchical semi-supervised contrastive learning for contamination-resistant anomaly detection. ECCV, 2022. paper

   Gaoang Wang, Yibing Zhan, Xinchao Wang, Mingli Song, and Klara Nahrstedt.

Model Selection

1. Automatic unsupervised outlier model selection. NIPS, 2021. paper

   Yue Zhao, Ryan Rossi, and Leman Akoglu.

2. Toward unsupervised outlier model selection. ICDM, 2022. paper

   Yue Zhao, Sean Zhang, and Leman Akoglu.

3. Unsupervised model selection for time-series anomaly detection. ICLR, 2023. paper

   Mononito Goswami, Cristian Ignacio Challu, Laurent Callot, Lenon Minorics, and Andrey Kan.

Gaussian Process

1. Deep anomaly detection with deviation networks. KDD, 2019. paper

   Guansong Pang, Chunhua Shen, and Anton van den Hengel.

2. Video anomaly detection and localization using hierarchical feature representation and Gaussian process regression. CVPR, 2015. paper

   Kai-Wen Cheng and Yie-Tarng Chen, and Wen-Hsien Fang.

3. Multidimensional time series anomaly detection: A GRU-based Gaussian mixture variational autoencoder approach. ACCV, 2018. paper

   Yifan Guo, Weixian Liao, Qianlong Wang, Lixing Yu, Tianxi Ji, and Pan Li.

4. Gaussian process regression-based video anomaly detection and localization with hierarchical feature representation. TIP, 2015. paper

   Kaiwen Cheng, Yie-Tarng Chen, and Wen-Hsien Fang.

Multi Task

1. Beyond dents and scratches: Logical constraints in unsupervised anomaly detection and localization. IJCV, 2022. paper

   Paul Bergmann, Kilian Batzner, Michael Fauser, David Sattlegger, and Carsten Steger.

2. Anomaly detection in video via self-supervised and multi-task learning. CVPR, 2021. paper

   Mariana-Iuliana Georgescu, Antonio Barbalau, Radu Tudor Ionescu, Fahad Shahbaz Khan, Marius Popescu, and Mubarak Shah.

3. Detecting semantic anomalies. AAAI, 2020. paper

   Faruk Ahmed and Aaron Courville.

4. MGADN: A multi-task graph anomaly detection network for multivariate time series. arXiv, 2022. paper

   Weixuan Xiong and Xiaochen Sun.

Outlier Exposure

1. Latent outlier exposure for anomaly detection with contaminated data. ICML, 2022. paper

   Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, and Stephan Mandt.

2. Deep anomaly detection with outlier exposure. ICLR, 2019. paper

   Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich.

3. A simple and effective baseline for out-of-distribution detection using abstention. ICLR, 2021. paper

   Sunil Thulasidasan, Sushil Thapa, Sayera Dhaubhadel, Gopinath Chennupati, Tanmoy Bhattacharya, and Jeff Bilmes.

4. Does your dermatology classifier know what it doesn’t know? Detecting the long-tail of unseen conditions. Medical Image Analysis, 2022. paper

   Abhijit 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. (1+ε)-class classification: An anomaly detection method for highly imbalanced or incomplete data sets. JMLR, 2021. paper

   Maxim Borisyak, Artem Ryzhikov, Andrey Ustyuzhanin, Denis Derkach, Fedor Ratnikov, and Olga Mineeva.

2. Deep semi-supervised anomaly detection. ICLR, 2020. paper

   Lukas Ruff, Robert A. Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, and Marius Kloft.

3. Online learning and sequential anomaly detection in trajectories. TPAMI, 2014. paper

   Rikard Laxhammar and Göran Falkman.

4. COPOD: Copula-based outlier detection. ICDM, 2020. paper

   Zheng Li, Yue Zhao, Nicola Botta, Cezar Ionescu, and Xiyang Hu.

5. ECOD: Unsupervised outlier detection using empirical cumulative distribution functions. TKDE, 2022. paper

   Zheng Li, Yue Zhao, Xiyang Hu, Nicola Botta, Cezar Ionescu, and George Chen.

6. GLAD: A global-to-local anomaly detector. WACV, 2023. paper

   Aitor Artola, Yannis Kolodziej, Jean-Michel Morel, and Thibaud Ehret.

Density Estimation

1. DenseHybrid: Hybrid anomaly detection for dense open-set recognition. ECCV, 2022. paper

   Matej Grcić, Petra Bevandić., and Siniša Šegvić.

2. Adaptive multi-stage density ratio estimation for learning latent space energy-based model. NIPS, 2022. paper

   Zhisheng Xiao, and Tian Han.

3. Ultrafast local outlier detection from a data stream with stationary region skipping. KDD, 2020. paper

   Susik Yoon, Jae-Gil Lee, and Byung Suk Lee.

4. A discriminative framework for anomaly detection in large videos. ECCV, 2016. paper

   Allison Del Giorno, J. Andrew Bagnell, and Martial Hebert.

5. Hierarchical density estimates for data clustering, visualization, and outlier detection. ACM Transactions on Knowledge Discovery from Data, 2015. paper

   Ricardo J. G. B. Campello, Davoud Moulavi, Arthur Zimek, and Jörg Sander.

6. Unsupervised anomaly detection by robust density estimation. AAAI, 2022. paper

   Boyang Liu, Pangning Tan, and Jiayu Zhou.

Memory Bank

1. Towards total recall in industrial anomaly detection. CVPR, 2022. paper

   Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, and Peter Gehler.

2. Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. ICCV, 2019. paper

   Dong Gong, Lingqiao Liu, Vuong Le, Budhaditya Saha, Moussa Reda Mansour, Svetha Venkatesh, and Anton van den Hengel.

Active Learning

1. DADMoE: Anomaly detection with mixture-of-experts from noisy labels. AAAI, 2023. paper

   Yue Zhao, Guoqing Zheng, Subhabrata Mukherjee, Robert McCann, and Ahmed Awadallah.

2. Incorporating expert feedback into active anomaly discovery. ICDM, 2016. paper

   Shubhomoy Das, Weng-Keen Wong, Thomas Dietterich, Alan Fern, and Andrew Emmott.

Cluster

1. MIDAS: Microcluster-based detector of anomalies in edge streams. AAAI, 2020. paper

   Siddharth Bhatia, Bryan Hooi, Minji Yoon, Kijung Shin, and Christos Faloutsos.

2. Multiple dynamic outlier-detection from a data stream by exploiting duality of data and queries. SIGMOD, 2021. paper

   Susik Yoon, Yooju Shin, Jae-Gil Lee, and Byung Suk Lee.

3. Dynamic local aggregation network with adaptive clusterer for anomaly detection. ECCV, 2022. paper

   Zhiwei Yang, Peng Wu, Jing Liu, and Xiaotao Liu.

4. Clustering and unsupervised anomaly detection with L2 normalized deep auto-encoder representations. IJCNN, 2018. paper

   Caglar Aytekin, Xingyang Ni, Francesco Cricri, and Emre Aksu.

5. Clustering driven deep autoencoder for video anomaly detection. ECCV, 2020. paper

   Yunpeng Chang, Zhigang Tu, Wei Xie, and Junsong Yuan.

Isolation

1. Isolation distributional kernel: A new tool for kernel based anomaly detection. KDD, 2020. paper

   Kai Ming Ting, Bicun Xu, Takashi Washio, and Zhihua Zhou.

2. AIDA: Analytic isolation and distance-based anomaly detection algorithm. arXiv, 2022. paper

   Luis Antonio Souto Arias, Cornelis W. Oosterlee, and Pasquale Cirillo.

Multimodal

1. Multimodal industrial anomaly detection via hybrid fusion. CVPR, 2023. paper

   Yue Wang, Jinlong Peng, Jiangning Zhang, Ran Yi, Yabiao Wang, and Chengjie Wang.

2. A multimodal anomaly detector for robot-assisted feeding using an LSTM-based variational autoencoder. ICRA, 2018. paper

   Daehyung Park, Yuuna Hoshi, and Charles C. Kemp.

Energy Model

1. Deep structured energy based models for anomaly detection. ICML, 2016. paper

   Shuangfei Zhai, Yu Cheng, Weining Lu, and Zhongfei Zhang.

2. Energy-based out-of-distribution detection. NIPS, 2020. paper

   Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li.

3. Learning neural set functions under the optimal subset oracle. NIPS, 2022. paper

   Zijing Ou, Tingyang Xu, Qinliang Su, Yingzhen Li, Peilin Zhao, and Yatao Bian.

Application

Finance

1. Antibenford subgraphs: Unsupervised anomaly detection in financial networks. KDD, 2022. paper

   Tianyi Chen and E. Tsourakakis.

2. Adversarial machine learning attacks against video anomaly detection systems. CVPR, 2022. paper

   Furkan Mumcu, Keval Doshi, and Yasin Yilmaz.

Point Cloud

1. Teacher-student network for 3D point cloud anomaly detection with few normal samples. arXiv, 2022. paper

   Jianjian Qin, Chunzhi Gu, Jun Yu, and Chao Zhang.

2. Teacher-student network for 3D point cloud anomaly detection with few normal samples. WACV, 2023. paper

   Paul Bergmann and David Sattlegger.

3. Anomaly Detection in 3D Point Clouds Using Deep Geometric Descriptors. WACV, 2023. paper

   Lokesh Veeramacheneni and Matias Valdenegro-Toro.

HPC

1. Anomaly detection using autoencoders in high performance computing systems. IAAI, 2019. paper

   Andrea Borghesi, Andrea Bartolini, Michele Lombardi, Michela Milano, and Luca Benini.

Intrusion

1. Intrusion detection using convolutional neural networks for representation learning. ICONIP, 2017. paper

   Hipeng Li, Zheng Qin, Kai Huang, Xiao Yang, and Shuxiong Ye.

Diagnosis

1. Transformer-based normative modelling for anomaly detection of early schizophrenia. NIPS, 2022. paper

   Pedro F Da Costa, Jessica Dafflon, Sergio Leonardo Mendes, João Ricardo Sato, M. Jorge Cardoso, Robert Leech, Emily JH Jones, and Walter H.L. Pinaya.