- 3.1 Density-based Methods
- 3.2 Reconstruction-based Methods
- 3.3 Classification-based Methods
- 3.4 Distance-based Methods
- 3.5 Gradient-based Methods
- 3.6 Discussion and Theoretical Analysis
[TPAMI-1998]
Parametric model fitting: from inlier characterization to outlier detection
Authors: Gaudenz Danuser, M. Stricker
Institution: Marine Biological Laboratory; Analytical, and Mathematical Services
[JESP-2018]
Detecting multivariate outliers: Use a robust variant of the mahalanobis distance
Authors: Christophe Leys, Olivier Klein, Yves Dominicy
Institution: University libre de Bruxelles; Ghent University
[ICML-2000]
Anomaly detection over noisy data using learned probability distributions
Authors: Eskin Eleazar
Institution: Columbia University
[ISI-2016]
Poisson factorization for peer-based anomaly detection
Authors: Melissa Turcotte, Juston Moore, Nick Heard, Aaron McPhall
Institution: Los Alamos National Laboratory; University of Bristol
[JASA-1991]
Review papers: Recent developments in non-parametric density estimation
Authors: Alan Julian Izenman
Institution: Temple University
[TKDE-2018]
Anomaly detection using local kernel density estimation and context-based regression
Authors: Weiming Hu, Jun Gao, Bing Li, Ou Wu, Junping Du, Stephen Maybank
Institution: Chinese Academy of Sciences; University of Chinese Academy of Sciences; Tianjin University; Birkbeck College
[ICLR-2018]
Deep autoencoding gaussian mixture model for Deep autoencoding gaussian mixture model for unsupervised anomaly detection
Authors: Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, Haifeng Chen
Institution: Washington State University; NEC Laboratories America
[CVPR-2019]
Latent Space Autoregression for Novelty Detection
Authors: Davide Abati, Angelo Porrello, Simone Calderara, Rita Cucchiara
Institution: University of Modena and Reggio Emilia
[NeurIPS-2018]
Generative probabilistic novelty detection with adversarial autoencoders
Authors: Stanislav Pidhorskyi, Ranya Almohsen, Donald A Adjeroh, Gianfranco Doretto
Institution: West Virginia University
[ECMLPKDD-2018]
Image anomaly detection with generative adversarial networks
Authors: Lucas Deecke, Robert VandermeulenLukas, RuffStephan Mandt, Marius Kloft
Institution: University of EdinburghEdinburghScotland; TU Kaiserslautern; Hasso Plattner Institute; University of California
[ICML-2015]
Variational inference with normalizing flows
Authors: Danilo Rezende, Shakir Mohamed
Institution: Google DeepMind
[TPAMI-2020]
Normalizing flows: An introduction and review of current methods
Authors: Ivan Kobyzev, Simon J.D. Prince, Marcus A. Brubaker
Institution: Borealis AI
[CVPR-2021]
Cutpaste: Self-supervised learning for anomaly detection and localization
Authors: Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, Tomas Pfister
Institution: Google Cloud AI Research
[CVPR-2021]
Multiresolution knowledge distillation for anomaly detection
Authors: Mohammadreza Salehi, Niousha Sadjadi, Soroosh Baselizadeh, Mohammad H. Rohban, Hamid R. Rabiee
Institution: Sharif University of Technology
[NeurIPS-2018]
A loss framework for calibrated anomaly detection
Authors: Aditya Krishna Menon, Robert C. Williamson
Institution: Australian National University
[CVPR-2021]
Multiattentional deepfake detection
Authors: Hanqing Zhao, Wenbo Zhou, Dongdong Chen, Tianyi Wei, Weiming Zhang, Nenghai Yu
Institution: University of Science and Technology of China; Microsoft Cloud AI
[AAAI-2020]
Ml-loo:Detecting adversarial examples with feature attribution
Authors: Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael Jordan
Institution: University of California
[CIKM-2020]
Towards generalizable deepfake detection with locality-aware autoencoder
Authors: Mengnan Du, Shiva Pentyala, Yuening Li, Xia Hu
Institution: Texas A&M University
[ICML-2016]
Deep structured energy based models for anomaly detection
Authors: Shuangfei Zhai, Yu Cheng, Weining Lu, Zhongfei Zhang
Institution: Binghamton Univeristy; IBM T. J. Watson Research Center; Tsinghua University
[2005]
Estimation of non-normalized statistical models by score matching
Authors: Aapo Hyv¡§arinen
Institution: BHelsinki Institute for Information Technology
[ICML-2011]
Bayesian learning via stochastic gradient langevin dynamics
Authors: Max Welling, Yee Whye Teh
Institution: University of California; UCL
[CVPR-2020]
High-frequency component helps explain the generalization of convolutional neural networks
Authors: Haohan Wang, Xindi Wu, Zeyi Huang, Eric P. Xing
Institution: UCarnegie Mellon University
[CNeurIPS-2019]
Adversarial examples are not bugs, they are features
Authors: Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry
Institution: MIT
[ICCV-2021]
Amplitudephase recombination: Rethinking robustness of convolutional neural networks in frequency domain
Authors: Guangyao Chen, Peixi Peng, Li Ma, Jia Li, Lin Du, Yonghong Tian
Institution: Peking University; Beihang University; AI Application Research Center Huawei
[CVPR-2021]
Spatial-phase shallow learning: rethinking face forgery detection in frequency domain
Authors: Honggu Liu, Xiaodan Li, Wenbo Zhou, Yuefeng Chen, Yuan He, Hui Xue, Weiming Zhang, Nenghai Yu
Institution: University of Science and Technology of China; Alibaba Group
[J. Signal Process. Syst.-2015]
Sparse coding with anomaly detection.
Authors: Amir Adler, Michael Elad, Yacov Hel-Or, Ehud Rivlin
Institution: Technion
[Multimedia Tools and Applications-2017]
Anomaly detection using sparse reconstruction in crowded scenes.
Authors: Ang Li, Zhenjiang Miao, Yigang Cen, Yi Cen
Institution: Beijing Jiaotong University, Beijing Key Laboratory, Minzu University of China
[IEEE-2014]
Adaptive Sparse Representations for Video Anomaly Detection.
Authors: Xuan Mo, Vishal Monga, Raja Bala, Zhigang Fan
Institution: Pennsylvania State University
[Pattern Recognition-2013]
AticleL1 norm based kpca for novelty detection.
Authors: Yingchao Xiao, Huangang Wanga, Wenli Xu, Junwu Zhou
Institution: Tsinghua University, Beijing General Research Institute of Mining & Metallurgy
[AAAI-2021]
Lren: Low-rank embedded network for sample-free hyperspectral anomaly detection.
Authors: Kai Jiang, Weiying Xie, Jie Lei, Tao Jiang, Yunsong Li
Institution: Xidian University
[NeurIPS-2018]
Generative probabilistic novelty detection with adversarial autoencoders.
Authors: Stanislav Pidhorskyi, Ranya Almohsen, Donald A Adjeroh, Gianfranco Doretto
Institution: West Virginia University
[Wireless Telecommunications Symposium-2018]
Autoencoderbased network anomaly detection.
Authors: Zhaomin Chen, Chai Kiat Yeo, Bu Sung Lee, Chiew Tong Lau
Institution: Nanyang Technological University
[Special Lecture on IE-2015]
Variational autoencoder based anomaly detection using reconstruction probability.
Authors: J. An and S. Cho
Institution: cannot open
[ICLR-W-2018]
Efficient GAN-Based Anomaly Detection.
Authors: Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, Vijay Ramaseshan Chandrasekhar
Institution: CentraleSup¡äelec, Nanyang Technological University, Carnegie Mellon University, Institute for Infocomm Research
[CVPR-2018]
Future frame prediction for anomaly detection¨Ca new baseline.
Authors: Wen Liu, Weixin Luo, Dongze Lian, Shenghua Gao
Institution: ShanghaiTech University
[CVPR-2019]
Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection.
Authors: Dong Gong, Lingqiao Liu, Vuong Le, Budhaditya Saha, Moussa Reda Mansour, Svetha Venkatesh, Anton van den Hengel
Institution: University of Adelaide, Deakin University, University of Western Australia
[CVPR-2020]
Learning Memory Guided Normality for Anomaly Detection.
Authors: Kevin Stephen, Varun Menon
Institution: Department of Information Technology, Pune Institute of Computer Technology, New York University
[ICLR-2020]
Robust subspace recovery layer for unsupervised anomaly detection.
Authors: Chieh-Hsin Lai, Dongmian Zou, Gilad Lerman
Institution: School of Mathematics University of Minnesota
[AAAI-2021]
Learning semantic context from normal samples for unsupervised anomaly detection.
Authors: Xudong Yan, Huaidong Zhang, Xuemiao Xu1, Xiaowei Hu, Pheng-Ann Heng
Institution: South China University of Technology, Ministry of Education Key Laboratory of Big Data and Intelligent Robot, Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information
[ICML-2019]
Anomaly detection with multiple-hypotheses predictions.
Authors: Duc Tam Nguyen, Zhongyu Lou, Michael Klar, Thomas Brox
Institution: University of Freiburg, Germany Corporate Research
[AAAI-2019]
Learning competitive and discriminative reconstructions for anomaly detection.
Authors: Kai Tian, Shuigeng Zhou, Jianping Fan, Jihong Guan
Institution: Fudan University, University of North Carolina at Charlotte, Tongji University
[CVPR-2018]
Adversarially learned one-class classifier for novelty detection.
Authors: Mohammad Sabokrou, Mohammad Khalooei, Mahmood Fathy, Ehsan Adeli
Institution: Institute for Research in Fundamental Sciences, Amirkabir University of Technology, Stanford University
[IEEE/CVF-2019]
Ocgan: One-class novelty detection using gans with constrained latent representations.
Authors: Pramuditha Perera, Ramesh Nallapati, Bing Xiang
Institution: Johns Hopkins University, AWS AI
[ECCV-2020]
Encoding structure-texture relation with p-net for anomaly detection in retinal images.
Authors: Kang Zhou, Yuting Xiao, Jianlong Yang, Jun Cheng, Wen Liu, Weixin Luo, Zaiwang Gu, Jiang Liu, Shenghua Gao
Institution: ShanghaiTech University, Chinese Academy of Sciences, Southern University, Shanghai Engineering Research Center of Intelligent Vision and Imaging
[arXiv preprint arXiv-2020]
Gan ensemble for anomaly detection.
Authors: Xu Han, Xiaohui Chen, Li-Ping Liu
Institution: Tufts University
[Journal of Artificial Intelligence Research-2002]
One-class classification: Concept learning in the absence of counter-examples.
Authors: Tax, David Martinus Johannes
Institution: TU Delft
[ICML-2018]
Deep one-class classification
Authors: Ruff, Lukas and Vandermeulen, Robert and Goernitz, Nico and Deecke, Lucas and Siddiqui, Shoaib Ahmed and Binder, Alexander and Muller, Emmanuel and Kloft, Marius
Institution: Humboldt University of Berlin; Hasso Plattner Institute; TU Kaiserslautern; TU Berlin; University of Edinburgh; Singapore University of Technology and Design
[CVPR-2021]
PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
Authors: Reiss, Tal and Cohen, Niv and Bergman, Liron and Hoshen, Yedid
Institution: The Hebrew University of Jerusalem
[CVPR-2019]
Gods: Generalized one-class discriminative subspaces for anomaly detection
Authors: Wang, Jue and Cherian, Anoop
Institution: Australian National University; Mitsubishi Electric Research Labs
[Machine Learning-2020]
Learning from positive and unlabeled data: A survey
Authors: Bekker, Jessa and Davis, Jesse
Institution: KU Leuven
[International Symposiums on Information Processing-2008]
Learning from positive and unlabeled examples: A survey
Authors: Zhang, Bangzuo and Zuo, Wanli
Institution: Jilin University; Northeast Normal University
[International Conference on Information, Intelligence, Systems and Applications-2019]
Positive and unlabeled learning algorithms and applications: A survey
Authors: Jaskie, Kristen and Spanias, Andreas
Institution: Arizona State University
[IJCAI-2003]
Learning to classify texts using positive and unlabeled data
Authors: Li, Xiaoli and Liu, Bing
Institution: National University of Singapore; University of Illinois at Chicago
[Bioinformatics-2006]
PSoL: a positive sample only learning algorithm for finding non-coding RNA genes
Authors: Wang, Chunlin and Ding, Chris and Meraz, Richard F and Holbrook, Stephen R
Institution: Lawrence Berkeley National Laboratory
[ICONIP-2012]
Learning from positive and unlabelled examples using maximum margin clustering
Authors: Chaudhari, Sneha and Shevade, Shirish
Institution: IBM Research; Indian Institute of Science
[Journal of Computers-2009]
Reliable Negative Extracting Based on kNN for Learning from Positive and Unlabeled Examples.
Authors: Zhang, Bangzuo and Zuo, Wanli
Institution: Jilin University; Northeast Normal University
[Journal of Information Science and Engineering-2014]
Clustering-based Method for Positive and Unlabeled Text Categorization Enhanced by Improved TFIDF.
Authors: Liu, Lu and Peng, Tao
Institution: University of Illinois at Urbana-Champaign Urbana; Jilin University
[arXiv-2018]
Instance-dependent pu learning by bayesian optimal relabeling
Authors: He, Fengxiang and Liu, Tongliang and Webb, Geoffrey I and Tao, Dacheng
Institution: University of Sydney
[AAAI-2019]
Learning competitive and discriminative reconstructions for anomaly detection
Authors: Tian, Kai and Zhou, Shuigeng and Fan, Jianping and Guan, Jihong
Institution: Fudan University; University of North Carolina; Tongji University
[CVPR-2019]
Graph convolutional label noise cleaner: Train a plug-and-play action classifier for anomaly detection
Authors: Zhong, Jia-Xing and Li, Nannan and Kong, Weijie and Liu, Shan and Li, Thomas H and Li, Ge
Institution: Peking University
[ICML-2015]
Learning from corrupted binary labels via class-probability estimation
Authors: Menon, Aditya and Van Rooyen, Brendan and Ong, Cheng Soon and Williamson, Bob
Institution: National ICT Australia; The Australian National University
[Artificial Intelligence and Statistics-2015]
A rate of convergence for mixture proportion estimation, with application to learning from noisy labels
Authors: Scott, Clayton
Institution: University of Michigan
[ICDM-2008]
Isolation forest
Authors: Liu, Fei Tony and Ting, Kai Ming and Zhou, Zhi-Hua
Institution: Monash University; Nanjing University
[NeurIPS-2018]
Deep anomaly detection using geometric transformations
Authors: Golan, Izhak and El-Yaniv, Ran
Institution: Israel Institute of Technology
[ICLR-2020]
Classification-based anomaly detection for general data
Authors: Bergman, Liron and Hoshen, Yedid
Institution: The Hebrew University of Jerusalem
[NeurIPS-2020]
Csi: Novelty detection via contrastive learning on distributionally shifted instances
Authors: Tack, Jihoon and Mo, Sangwoo and Jeong, Jongheon and Shin, Jinwoo
Institution: KAIST
[CVPR-2021]
Anomaly detection in video via self-supervised and multi-task learning
Authors: Georgescu, Mariana-Iuliana and Barbalau, Antonio and Ionescu, Radu Tudor and Khan, Fahad Shahbaz and Popescu, Marius and Shah, Mubarak
Institution: University of Bucharest; Abu Dhabi; SecurifAI; University of Central Florida
[CVPR-2019]
Object-centric auto-encoders and dummy anomalies for abnormal event detection in video
Authors: Ionescu, Radu Tudor and Khan, Fahad Shahbaz and Georgescu, Mariana-Iuliana and Shao, Ling
Institution: IIAI; University of Bucharest; SecurifAI
[PHM Society European Conference, 2014]
Anomaly detection using self-organizing maps-based k-nearest neighbor algorithm
Authors: J. Tian, M. H. Azarian, and M. Pecht
Institution: Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD, 20742, U.S.A.
[GI/ITG Workshop MMBnet, pp. 13¨C14, 2007]
Traffic anomaly detection using k-means clustering
Authors: G. Munz, S. Li, and G. Carle
Institution: Wilhelm Schickard Institute for Computer Science; University of Tuebingen, Germany
[International conference on networked digital technologies, pp. 135¨C145, Springer,2012]
Unsupervised clustering approach for network anomaly detection
Authors: I. Syarif, A. Prugel-Bennett, and G. Wills
Institution: School of Electronics and Computer Science, University of Southampton, UK; Eletronics Engineering Polytechnics Institute of Surabaya, Indonesia
[ECCV-2020]
Back-propagated gradient representations for anomaly detection
Authors: G. Kwon, M. Prabhushankar, D. Temel, and G. AlRegib
Institution: Georgia Institute of Technology, Atlanta, GA 30332, USA
[ICML-2018]
Open category detection with pac guarantees
Authors: S. Liu, R. Garrepalli, T. Dietterich, A. Fern, and D. Hendrycks
Institution: Department of Statistics, Oregon State University, Oregn, USA School of EECS, Oregon State University, Oregon, USA University of California, Berkeley, California USA
[ICML-2021]
Learning bounds for open-set learning
Authors: Z. Fang, J. Lu, A. Liu, F. Liu, and G. Zhang
Institution: AAII, University of Technology Sydney.