Anomaly Detection: The process of detectingdata instances that significantly deviate from the majority of the whole dataset.
Contributed by Chunyang Zhang.
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A survey of single-scene video anomaly detection. TPAMI, 2022. paper
Bharathkumar Ramachandra, Michael J. Jones, and Ranga Raju Vatsavai.
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Deep learning for anomaly detection: A review. ACM Computing Surveys, 2022. paper
Guansong Pang, Chunhua Shen, Longbing Cao, and Anton Van Den Hengel.
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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.
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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.
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Anomaly detection in autonomous driving: A survey. CVPR, 2022. paper
Daniel Bogdoll, Maximilian Nitsche, and J. Marius Zöllner.
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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.
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Transformers in time series: A survey. arXiv, 2022. paper
Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, and Liang Sun.
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A survey on explainable anomaly detection. arXiv, 2022. paper
Zhong Li, Yuxuan Zhu, and Matthijs van Leeuwen.
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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.
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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.
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GAN-based anomaly detection: A review. Neurocomputing, 2022. paper
Xuan Xia, Xizhou Pan, Nan Lia, Xing He, Lin Ma, Xiaoguang Zhang, and Ning Ding.
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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.
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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.
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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.
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Survey on anomaly detection using data mining techniques. Procedia Computer Science, 2015. paper
Shikha Agrawal and Jitendra Agrawal.
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Graph based anomaly detection and description: A survey. Data Mining and Knowledge Discovery, 2015. paper
Leman Akoglu, Hanghang Tong, and Danai Koutra.
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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.
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Graph-based time-series anomaly detection: A Survey. arXiv, 2023. paper
Thi Kieu Khanh Ho, Ali Karami, and Narges Armanfard.
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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.
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A comprehensive survey of deep transfer learning for anomaly detection in industrial time series: Methods, applications, and directions. arXiv, 2023. paper
Peng Yan, Ahmed Abdulkadir, Matthias Rosenthal, Gerrit A. Schatte, Benjamin F. Grewe, and Thilo Stadelmann.
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Survey on video anomaly detection in dynamic scenes with moving cameras. arXiv, 2023. paper
Runyu Jiao, Yi Wan, Fabio Poiesi, and Yiming Wang.
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Physics-informed machine learning for data anomaly detection, classification, localization, and mitigation: A review, challenges, and path forward. arXiv, 2023. paper
Mehdi Jabbari Zideh, Paroma Chatterjee, and Anurag K. Srivastava.
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Detecting and learning out-of-distribution data in the open world: Algorithm and theory. Thesis, 2023. Ph.D.
Yiyou Sun.
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Meta-survey on outlier and anomaly detection. arXiv, 2023. paper
Madalina Olteanu, Fabrice Rossi, and Florian Yger.
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Anomaly detection in surveillance videos: A thematic taxonomy of deep models, review and performance analysis. Artificial Intelligence Review, 2023. paper
S. Chandrakala, K. Deepak, and G. Revathy.
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Revisiting VAE for unsupervised time series anomaly detection: A frequency perspective. WWW, 2024. paper
Zexin Wang, Changhua Pei, Minghua Ma, Xin Wang, Zhihan Li, Dan Pei, Saravan Rajmohan, Dongmei Zhang, Qingwei Lin, Haiming Zhang, Jianhui Li, and Gaogang Xie.
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Can tree based approaches surpass deep learning in anomaly detection? A benchmarking study. arXiv, 2024. paper
Santonu Sarkar, Shanay Mehta, Nicole Fernandes, Jyotirmoy Sarkar, and Snehanshu Saha.
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Large language models for forecasting and anomaly detection: A systematic literature review. arXiv, 2024. paper
Jing Su, Chufeng Jiang, Xin Jin, Yuxin Qiao, Tingsong Xiao, Hongda Ma, Rong Wei, Zhi Jing, Jiajun Xu, and Junhong Lin.
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A survey of graph neural networks in real world: Imbalance, noise, privacy and OOD challenges. arXiv, 2024. paper
Wei Ju, Siyu Yi, Yifan Wang, Zhiping Xiao, Zhengyang Mao, Hourun Li, Yiyang Gu, Yifang Qin, Nan Yin, Senzhang Wang, Xinwang Liu, Xiao Luo, Philip S. Yu, and Ming Zhang.
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Anomaly detection in graph structured data: A survey. arXiv, 2024. paper
Prabin B Lamichhane and William Eberle.
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Graph regularized autoencoder and its application in unsupervised anomaly detection. TPAMI, 2022. paper
Imtiaz Ahmed, Travis Galoppo, Xia Hu, and Yu Ding.
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Innovations autoencoder and its application in one-class anomalous sequence detection. JMLR, 2022. paper
Xinyi Wang and Lang Tong.
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Autoencoders-A comparative analysis in the realm of anomaly detection. CVPR, 2022. paper
Sarah Schneider, Doris Antensteiner, Daniel Soukup, and Matthias Scheutz.
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Attention guided anomaly localization in images. ECCV, 2020. paper
Shashanka Venkataramanan, Kuan-Chuan Peng, Rajat Vikram Singh, and Abhijit Mahalanobis.
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Latent space autoregression for novelty detection. CVPR, 2018. paper
Davide Abati, Angelo Porrello, Simone Calderara, and Rita Cucchiara.
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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.
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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.
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Latent feature learning via autoencoder training for automatic classification configuration recommendation. KBS, 2022. paper
Liping Deng and Mingqing Xiao.
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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.
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Anomaly detection with robust deep autoencoders. KDD, 2017. paper
Chong Zhou and Randy C. Paffenroth.
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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.
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Spatio-temporal autoencoder for video anomaly detection. MM, 2017. paper
Yiru Zhao, Bing Deng, Chen Shen, Yao Liu, Hongtao Lu, and Xiansheng Hua.
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Learning discriminative reconstructions for unsupervised outlier removal. ICCV, 2015. paper
Yan Xia, Xudong Cao, Fang Wen, Gang Hua, and Jian Sun.
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Outlier detection with autoencoder ensembles. ICDM, 2017. paper
Jinghui Chen, Saket Sathey, Charu Aggarwaly, and Deepak Turaga.
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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.
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Classification-reconstruction learning for open-set recognition. CVPR, 2019. paper
Ryota Yoshihashi, Shaodi You, Wen Shao, Makoto Iida, Rei Kawakami, and Takeshi Naemura.
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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.
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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.
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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.
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Deep autoencoding one-class time series anomaly detection. ICASSP, 2023. paper
Xudong Mou, Rui Wang, Tiejun Wang, Jie Sun, Bo Li, Tianyu Wo, and Xudong Liu.
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Reconstruction error-based anomaly detection with few outlying examples. arXiv, 2023. paper
Fabrizio Angiulli, Fabio Fassetti, and Luca Ferragina.
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LARA: A light and anti-overfitting retraining approach for unsupervised anomaly detection. arXiv, 2023. paper
Feiyi Chen, Zhen Qing, Yingying Zhang, Shuiguang Deng, Yi Xiao, Guansong Pang, and Qingsong Wen.
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FMM-Head: Enhancing autoencoder-based ECG anomaly detection with prior knowledge. arXiv, 2023. paper
Giacomo Verardo, Magnus Boman, Samuel Bruchfeld, Marco Chiesa, Sabine Koch, Gerald Q. Maguire Jr., and Dejan Kostic.
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Online multi-view anomaly detection with disentangled product-of-experts modeling. MM, 2023. paper
Hao Wang, Zhiqi Cheng, Jingdong Sun, Xin Yang, Xiao Wu, Hongyang Chen, and Yan Yang.
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Fast particle-based anomaly detection algorithm with variational autoencoder. arXiv, 2023. paper
Ryan Liu, Abhijith Gandrakota, Jennifer Ngadiuba, Maria Spiropulu, and Jean-Roch Vlimant.
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Dynamic erasing network based on multi-scale temporal features for weakly supervised video anomaly detection. arXiv, 2023. paper
Chen Zhang, Guorong Li, Yuankai Qi, Hanhua Ye, Laiyun Qing, Ming-Hsuan Yang, and Qingming Huang.
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ACVAE: A novel self-adversarial variational auto-encoder combined with contrast learning for time series anomaly detection. Neural Networks, 2023. paper
Xiaoxia Zhang, Shang Shi, HaiChao Sun, Degang Chen, Guoyin Wang, and Kesheng Wu.
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Dual-constraint autoencoder and adaptive weighted similarity spatial attention for unsupervised anomaly detection. TII, 2023. paper
Ruifan Zhang, Hao Wang, Mingyao Feng, Yikun Liu, and Gongping Yang.
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Integrating adversarial training strategies into deep autoencoders: A novel aeroengine anomaly detection framework. EAAI, 2024. paper
Lin Lin, Lizheng Zu, Song Fu, Yikun Liu, Sihao Zhang, Shiwei Suo, and Changsheng Tong.
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An outlier-resilient autoencoder for representing high-dimensional and incomplete data. TETCI, 2024. paper
Di Wu, Yuanpeng Hu, Kechen Liu, Jing Li, Xianmin Wang, Song Deng, Nenggan Zheng, and Xin Luo.
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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.
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GAN ensemble for anomaly detection. AAAI, 2021. paper
Han, Xu, Xiaohui Chen, and Liping Liu.
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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.
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GAN-based anomaly detection in imbalance problems. ECCV, 2020. paper
Junbong Kim, Kwanghee Jeong, Hyomin Choi, and Kisung Seo.
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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.
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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.
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Adversarially learned anomaly detection. ICDM, 2018. paper
Houssam Zenati, Manon Romain, Chuan-Sheng Foo, Bruno Lecouat, and Vijay Chandrasekhar.
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BeatGAN: Anomalous rhythm detection using adversarially generated time series. IJCAI, 2019. paper
Bin Zhou, Shenghua Liu, Bryan Hooi, Xueqi Cheng, and Jing Ye.
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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.
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USAD: Unsupervised anomaly detection on multivariate time series. KDD, 2020. paper
Julien Audibert, Pietro Michiardi, Frédéric Guyard, Sébastien Marti, and Maria A. Zuluaga.
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Anomaly detection with generative adversarial networks for multivariate time series. ICLR, 2018. paper
Dan Li, Dacheng Chen, Jonathan Goh, and See-kiong Ng.
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Efficient GAN-based anomaly detection. ICLR, 2018. paper
Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, and Vijay Ramaseshan Chandrasekhar.
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GANomaly: Semi-supervised anomaly detection via adversarial training. ACCV, 2019. paper
Akcay, Samet, Amir Atapour-Abarghouei, and Toby P. Breckon.
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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.
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OCGAN: One-class novelty detection using GANs with constrained latent representations. CVPR, 2019. paper
Pramuditha Perera, Ramesh Nallapati, and Bing Xiang.
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Adversarially learned one-class classifier for novelty detection. CVPR, 2018. paper
Mohammad Sabokrou, Mohammad Khalooei, Mahmood Fathy, and Ehsan Adeli.
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Generative probabilistic novelty detection with adversarial autoencoders. NIPS, 2018. paper
Stanislav Pidhorskyi, Ranya Almohsen, Donald A. Adjeroh, and Gianfranco Doretto.
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Image anomaly detection with generative adversarial networks. ECML PKDD, 2018. paper
Lucas Deecke, Robert Vandermeulen, Lukas Ruff, Stephan Mandt, and Marius Kloft.
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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.
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Truncated affinity maximization: One-class homophily modeling for graph anomaly detection. arXiv, 2023. paper
Qiao Hezhe and Pang Guansong.
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Anomaly detection under contaminated data with contamination-immune bidirectional GANs. TKDE, 2024. paper
Qinliang Su, Bowen Tian, Hai Wan, and Jian Yin.
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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.
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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.
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Future frame prediction network for video anomaly detection. TPAMI, 2022. paper
Weixin Luo, Wen Liu, Dongze Lian, and Shenghua Gao.
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Graph-augmented normalizing flows for anomaly detection of multiple time series. ICLR, 2022. paper
Enyan Dai and Jie Chen.
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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.
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A modular and unified framework for detecting and localizing video anomalies. WACV, 2022. paper
Keval Doshi and Yasin Yilmaz.
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Video anomaly detection with compact feature sets for online performance. TIP, 2017. paper
Roberto Leyva, Victor Sanchez, and Chang-Tsun Li.
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U-Flow: A U-shaped normalizing flow for anomaly detection with unsupervised threshold. arXiv, 2017. paper
Matías Tailanian, Álvaro Pardo, and Pablo Musé.
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Bi-directional frame interpolation for unsupervised video anomaly detection. WACV, 2023. paper
Hanqiu Deng, Zhaoxiang Zhang, Shihao Zou, and Xingyu Li.
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AE-FLOW: Autoencoders with normalizing flows for medical images anomaly detection. ICLR, 2023. paper
Yuzhong Zhao, Qiaoqiao Ding, and Xiaoqun Zhang.
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A video anomaly detection framework based on appearance-motion semantics representation consistency. ICASSP, 2023. paper
Xiangyu Huang, Caidan Zhao, and Zhiqiang Wu.
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Fully convolutional cross-scale-flows for image-based defect detection. WACV, 2022. paper
Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn, and Bastian Wandt.
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CFLOW-AD: Real-time unsupervised anomaly detection with localization via conditional normalizing flows. WACV, 2022. paper
Denis Gudovskiy, Shun Ishizaka, and Kazuki Kozuka.
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Same same but DifferNet: Semi-supervised defect detection with normalizing flows. WACV, 2021. paper
Marco Rudolph, Bastian Wandt, and Bodo Rosenhahn.
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Normalizing flow based feature synthesis for outlier-aware object detection. CVPR, 2023. paper
Nishant Kumar, Siniša Šegvić, Abouzar Eslami, and Stefan Gumhold.
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DyAnNet: A scene dynamicity guided self-trained video anomaly detection network. WACV, 2023. paper
Kamalakar Vijay Thakare, Yash Raghuwanshi, Debi Prosad Dogra, Heeseung Choi, and Ig-Jae Kim.
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Multi-scale spatial-temporal interaction network for video anomaly detection. arXiv, 2023. paper
Zhiyuan Ning, Zhangxun Li, and Liang Song.
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MSFlow: Multi-scale flow-based framework for unsupervised anomaly detection. arXiv, 2023. paper
Yixuan Zhou, Xing Xu, Jingkuan Song, Fumin Shen, and Hengtao Shen.
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PyramidFlow: High-resolution defect contrastive localization using pyramid normalizing flow. CVPR, 2023. paper
Jiarui Lei, Xiaobo Hu, Yue Wang, and Dong Liu.
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Topology-matching normalizing flows for out-of-distribution detection in robot learning. CoRL, 2023. paper
Jianxiang Feng, Jongseok Lee, Simon Geisler, Stephan Günnemann, and Rudolph Triebel.
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Video anomaly detection via spatio-temporal pseudo-anomaly generation : A unified approach. arXiv, 2023. paper
Ayush K. Rai, Tarun Krishna, Feiyan Hu, Alexandru Drimbarean, Kevin McGuinness, Alan F. Smeaton, and Noel E. O'Connor.
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Self-supervised normalizing flows for image anomaly detection and localization. CVPR, 2023. paper
Li-Ling Chiu and Shang-Hong Lai.
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Normalizing flows for human pose anomaly detection. ICCV, 2023. paper
Or Hirschorn and Shai Avidan.
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Hierarchical Gaussian mixture normalizing flow modeling for unified anomaly detection. arXiv, 2024. paper
Xincheng Yao, Ruoqi Li, Zefeng Qian, Lu Wang, and Chongyang Zhang.
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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.
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Diffusion models for medical anomaly detection. MICCAI, 2022. paper
Julia Wolleb, Florentin Bieder, Robin Sandkühler, and Philippe C. Cattin.
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DiffusionAD: Denoising diffusion for anomaly detection. arXiv, 2023. paper
Hui Zhang, Zheng Wang, Zuxuan Wu, Yugang Jiang.
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Anomaly detection with conditioned denoising diffusion models. arXiv, 2023. paper
Arian Mousakhan, Thomas Brox, and Jawad Tayyub.
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Unsupervised out-of-distribution detection with diffusion inpainting. ICML, 2023. paper
Zhenzhen Liu, Jin Peng Zhou, Yufan Wang, and Kilian Q. Weinberger.
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On diffusion modeling for anomaly detection. ICLR, 2024. paper
Victor Livernoche, Vineet Jain, Yashar Hezaveh, and Siamak Ravanbakhsh.
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Mask, stitch, and re-sample: Enhancing robustness and generalizability in anomaly detection through automatic diffusion models. arXiv, 2023. paper
Cosmin I. Bercea, Michael Neumayr, Daniel Rueckert, and Julia A. Schnabel.
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Unsupervised anomaly detection in medical images using masked diffusion model. arXiv, 2023. paper
Hasan Iqbal, Umar Khalid, Jing Hua, and Chen Chen.
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Unsupervised anomaly detection in medical images using masked diffusion model. arXiv, 2023. paper
Hasan Iqbal, Umar Khalid, Jing Hua, and Chen Chen.
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ImDiffusion: Imputed diffusion models for multivariate time series anomaly detection. arXiv, 2023. paper
Yuhang Chen, Chaoyun Zhang, Minghua Ma, Yudong Liu, Ruomeng Ding, Bowen Li, Shilin He, Saravan Rajmohan, Qingwei Lin, and Dongmei Zhang.
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Multimodal motion conditioned diffusion model for skeleton-based video anomaly detection. ICCV, 2023. paper
Alessandro Flaborea, Luca Collorone, Guido Maria D’Amely di Melendugno, Stefano D’Arrigo, Bardh Prenkaj, and Fabio Galasso.
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LafitE: Latent diffusion model with feature editing for unsupervised multi-class anomaly detection. arXiv, 2023. paper
Haonan Yin, Guanlong Jiao, Qianhui Wu, Borje F. Karlsson, Biqing Huang, and Chin Yew Lin.
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Diffusion models for counterfactual generation and anomaly detection in brain images. arXiv, 2023. paper
Alessandro Fontanella, Grant Mair, Joanna Wardlaw, Emanuele Trucco, and Amos Storkey.
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Imputation-based time-series anomaly detection with conditional weight-incremental diffusion models. KDD, 2023. paper
Chunjing Xiao, Zehua Gou, Wenxin Tai, Kunpeng Zhang, and Fan Zhou.
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MadSGM: Multivariate anomaly detection with score-based generative models. CIKM, 2023. paper
Haksoo Lim, Sewon Park, Minjung Kim, Jaehoon Lee, Seonkyu Lim, and Noseong Park.
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Modality cycles with masked conditional diffusion for unsupervised anomaly segmentation in MRI. MICCAI, 2023. paper
Ziyun Liang, Harry Anthony, Felix Wagner, and Konstantinos Kamnitsas.
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Controlled graph neural networks with denoising diffusion for anomaly detection. Expert Systems with Applications, 2023. paper
Xuan Li, Chunjing Xiao, Ziliang Feng, Shikang Pang, Wenxin Tai, and Fan Zhou.
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Unsupervised surface anomaly detection with diffusion probabilistic model. ICCV, 2023. paper
Matic Fučka, Vitjan Zavrtanik, and Danijel Skočaj.
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Transfusion -- A transparency-based diffusion model for anomaly detection. ECCV, 2024. paper
Ziyun Liang, Harry Anthony, Felix Wagner, and Konstantinos Kamnitsas.
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Unsupervised anomaly detection using aggregated normative diffusion. arXiv, 2023. paper
Alexander Frotscher, Jaivardhan Kapoor, Thomas Wolfers, and Christian F. Baumgartner.
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Adversarial denoising diffusion model for unsupervised anomaly detection. arXiv, 2023. paper
Jongmin Yu, Hyeontaek Oh, and Jinhong Yang.
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Guided reconstruction with conditioned diffusion models for unsupervised anomaly detection in brain MRIs. arXiv, 2023. paper
Finn Behrendt, Debayan Bhattacharya, Robin Mieling, Lennart Maack, Julia Krüger, Roland Opfer, and Alexander Schlaefer.
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DiAD: A diffusion-based framework for multi-class anomaly detection. arXiv, 2023. paper
Haoyang He, Jiangning Zhang, Hongxu Chen, Xuhai Chen, Zhishan Li, Xu Chen, Yabiao Wang, Chengjie Wang, and Lei Xie.
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Feature prediction diffusion model for video anomaly detection. ICCV, 2023. paper
Cheng Yan, Shiyu Zhang, Yang Liu, Guansong Pang, and Wenjun Wang.
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Removing anomalies as noises for industrial defect localization. ICCV, 2023. paper
Fanbin Lu, Xufeng Yao, Chi-Wing Fu, and Jiaya Jia.
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DATAELIXIR: Purifying poisoned dataset to mitigate backdoor attacks via diffusion models. AAAI, 2024. paper
Jiachen Zhou, Peizhuo Lv, Yibing Lan, Guozhu Meng, Kai Chen, and Hualong Ma.
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Controlled graph neural networks with denoising diffusion for anomaly detection. Expert Systems with Applications, 2024. paper
Xuan Li, Chunjing Xiao, Ziliang Feng, Shikang Pang, Wenxin Tai, and Fan Zhou.
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D3AD: Dynamic denoising diffusion probabilistic model for anomaly detection. arXiv, 2024. paper
Justin Tebbe and Jawad Tayyub.
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TauAD: MRI-free Tau anomaly detection in PET imaging via conditioned diffusion models. arXiv, 2024. paper
Lujia Zhong, Shuo Huang, Jiaxin Yue, Jianwei Zhang, Zhiwei Deng, Wenhao Chi, and Yonggang Shi.
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Vague prototype-oriented diffusion model for multi-class anomaly detection. ICML, 2024. paper
Yuxin Li, Yaoxuan Feng, Bo Chen, Wenchao Chen, Yubiao Wang, Xinyue Hu, baolin sun, Chunhui Qu, and Mingyuan Zhou.
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Counterfactual data augmentation with denoising diffusion for graph anomaly detection. TCSS, 2024. paper
Chunjing Xiao, Shikang Pang, Xovee Xu, Xuan Li, Goce Trajcevski, and Fan Zhou.
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R3D-AD: Reconstruction via diffusion for 3D anomaly detection. ECCV, 2024. paper
Zheyuan Zhou, Le Wang, Naiyu Fang, Zili Wang, Lemiao Qiu, and Shuyou Zhang.
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Video anomaly detection via prediction network with enhanced spatio-temporal memory exchange. ICASSP, 2022. paper
Guodong Shen, Yuqi Ouyang, and Victor Sanchez.
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TranAD: Deep transformer networks for anomaly detection in multivariate time series data. VLDB, 2022. paper
Shreshth Tuli, Giuliano Casale, and Nicholas R. Jennings.
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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.
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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.
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Anomaly transformer: Time series anomaly detection with association discrepancy. ICLR, 2022. paper
Jiehui Xu, Haixu Wu, Jianmin Wang, and Mingsheng Long.
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Constrained adaptive projection with pretrained features for anomaly detection. IJCAI, 2022. paper
Xingtai Gui, Di Wu, Yang Chang, and Shicai Fan.
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Self-training multi-sequence learning with transformer for weakly supervised video anomaly detection. AAAI, 2022. paper
Shuo Li, Fang Liu, and Licheng Jiao.
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Beyond outlier detection: Outlier interpretation by attention-guided triplet deviation network. WWW, 2021. paper
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