Most cited papers in Anomaly Detection Techniques and Applications
What are the most cited papers in Anomaly Detection Techniques and Applications?
The most cited paper in Anomaly Detection Techniques and Applications is "Deep learning" (Yann LeCun, Yoshua Bengio, Geoffrey E. Hinton, 2015), with 84,939 citations. Next come "LIBSVM" (41,500) and "SMOTE: Synthetic Minority Over-sampling Technique" (32,759).
Showing 50 of 98 papers. Every paper with more than 1,731 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Deep learning Yann LeCun, Yoshua Bengio, Geoffrey E. Hinton · Nature | 26 May 2015 | 84,939 |
| 2 | LIBSVM Chih-Chung Chang, Chih‐Jen Lin · ACM Transactions on Intelligent Systems and Technology | Apr 2011 | 41,500 |
| 3 | SMOTE: Synthetic Minority Over-sampling Technique Nitesh V. Chawla, Kevin W. Bowyer, Lawrence Hall et al. · Journal of Artificial Intelligence Research | Jun 2002 | 32,759 |
| 4 | Rethinking the Inception Architecture for Computer Vision Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe et al. | Jun 2016 | 31,624 |
| 5 | MobileNetV2: Inverted Residuals and Linear Bottlenecks Mark Sandler, Andrew Howard, Menglong Zhu et al. | Jun 2018 | 26,808 |
| 6 | GAN（Generative Adversarial Nets） 柴田 淳司 · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics | 15 Oct 2017 | 21,691 |
| 7 | Anomaly detection Varun Chandola, Arindam Banerjee, Vipin Kumar · ACM Computing Surveys | Jul 2009 | 11,623 |
| 8 | Learning from Imbalanced Data Haibo He, Edwardo A. Garcia · IEEE Transactions on Knowledge and Data Engineering | Jul 2009 | 10,349 |
| 9 | Machine learning: Trends, perspectives, and prospects Michael I. Jordan, Tom M. Mitchell · Science | 16 Jul 2015 | 10,020 |
| 10 | Explaining and Harnessing Adversarial Examples Ian Goodfellow, Jonathon Shlens, Christian Szegedy · arXiv (Cornell University) | 20 Dec 2014 | 8,140 |
| 11 | Review of deep learning: concepts, CNN architectures, challenges, applications, future directions Laith H. Alzubaidi, Jinglan Zhang, Amjad J. Humaidi et al. · Journal Of Big Data | 31 Mar 2021 | 7,920 |
| 12 | Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields Zhe Cao, Tomas Simon, Shih-En Wei et al. | Jul 2017 | 7,513 |
| 13 | Learning Deep Architectures for AI Yoshua Bengio · Foundations and Trends® in Machine Learning | 2009 | 6,986 |
| 14 | Large-Scale Video Classification with Convolutional Neural Networks Andrej Karpathy, George D. Toderici, Sanketh Shetty et al. | Jun 2014 | 6,403 |
| 15 | Isolation Forest Fei Tony Liu, Kai Ming Ting, Zhi‐Hua Zhou | Dec 2008 | 6,132 |
| 16 | Deep High-Resolution Representation Learning for Human Pose Estimation Ke Sun, Bin Xiao, Dong Liu et al. | Jun 2019 | 5,840 |
| 17 | Intriguing properties of neural networks Christian Szegedy, Wojciech Zaremba, Ilya Sutskever et al. · arXiv (Cornell University) | 21 Dec 2013 | 5,734 |
| 18 | Machine Learning: Algorithms, Real-World Applications and Research Directions Iqbal H. Sarker · SN Computer Science | 22 Mar 2021 | 5,413 |
| 19 | Two-Stream Convolutional Networks for Action Recognition in Videos Karen Simonyan, Andrew Zisserman · arXiv (Cornell University) | 9 Jun 2014 | 5,362 |
| 20 | LOF Markus M. Breunig, Hans‐Peter Kriegel, Raymond T. Ng et al. · ACM SIGMOD Record | 16 May 2000 | 5,270 |
| 21 | OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields Zhe Cao, Gines Hidalgo, Tomas Simon et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | 17 Jul 2019 | 5,255 |
| 22 | A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects Zewen Li, Fan Liu, Wenjie Yang et al. · IEEE Transactions on Neural Networks and Learning Systems | 10 Jun 2021 | 5,150 |
| 23 | A detailed analysis of the KDD CUP 99 data set Mahbod Tavallaee, Ebrahim Bagheri, Wei Lu et al. | Jul 2009 | 5,024 |
| 24 | On Assessing ML Model Robustness: A Methodological Framework (Academic Track) Awadid, Afef, Robert, Boris · OASIcs : OpenAccess Series in Informatics | 2025 | 4,781 |
| 25 | LOF Markus M. Breunig, Hans‐Peter Kriegel, Raymond T. Ng et al. | 16 May 2000 | 4,056 |
| 26 | Knowledge Distillation: A Survey Jianping Gou, Baosheng Yu, Stephen J. Maybank et al. · International Journal of Computer Vision | 22 Mar 2021 | 3,806 |
| 27 | SlowFast Networks for Video Recognition Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik et al. | Oct 2019 | 3,782 |
| 28 | Robust Recovery of Subspace Structures by Low-Rank Representation Guangcan Liu, Zhouchen Lin, Shuicheng Yan et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | 10 Apr 2012 | 3,718 |
| 29 | A Closer Look at Spatiotemporal Convolutions for Action Recognition Du Tran, Heng Wang, Lorenzo Torresani et al. | Jun 2018 | 3,637 |
| 30 | Support Vector Data Description David M. J. Tax, Robert P. W. Duin · Machine Learning | 16 Dec 2003 | 3,551 |
| 31 | Practical Black-Box Attacks against Machine Learning Nicolas Papernot, Patrick Drew McDaniel, Ian Goodfellow et al. | 31 Mar 2017 | 3,549 |
| 32 | Learning realistic human actions from movies Ivan Laptev, Marcin Marszałek, Cordelia Schmid et al. | Jun 2008 | 3,525 |
| 33 | A Survey of Outlier Detection Methodologies Joanna Hodge, Jim Austin · Artificial Intelligence Review | Oct 2004 | 3,405 |
| 34 | Deep learning for time series classification: a review Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber et al. · Data Mining and Knowledge Discovery | 2 Mar 2019 | 3,344 |
| 35 | Tracking-Learning-Detection Zdenek Kalal, Krystian Mikolajczyk, Jiřı́ Matas · IEEE Transactions on Pattern Analysis and Machine Intelligence | 13 Dec 2011 | 3,308 |
| 36 | A survey of deep neural network architectures and their applications Weibo Liu, Zidong Wang, Xiaohui Liu et al. · Neurocomputing | 18 Dec 2016 | 3,298 |
| 37 | A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection Anna L. Buczak, Erhan Guven · IEEE Communications Surveys & Tutorials | 26 Oct 2015 | 3,296 |
| 38 | Pedestrian Detection: An Evaluation of the State of the Art Piotr Dollár, Christian Wojek, Bernt Schiele et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | 15 Aug 2011 | 3,283 |
| 39 | Learning patterns of activity using real-time tracking Chris Stauffer, W. Eric L. Grimson · IEEE Transactions on Pattern Analysis and Machine Intelligence | 2000 | 3,248 |
| 40 | Siamese Neural Networks for One-shot Image Recognition Gregory E. Koch, Richard S. Zemel, Ruslan Salakhutdinov | 2015 | 3,246 |
| 41 | Are Transformers Effective for Time Series Forecasting? Ailing Zeng, Muxi Chen, Lei Zhang et al. · Proceedings of the AAAI Conference on Artificial Intelligence | 26 Jun 2023 | 3,110 |
| 42 | Ensemble learning: A survey Omer Sagi, Lior Rokach · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery | 27 Feb 2018 | 3,107 |
| 43 | State-of-the-art in artificial neural network applications: A survey Oludare Isaac Abiodun, Aman Jantan, Abiodun Esther Omolara et al. · Heliyon | Nov 2018 | 3,086 |
| 44 | NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis Amir Shahroudy, Jun Liu, Tian-Tsong Ng et al. | Jun 2016 | 3,067 |
| 45 | Recognizing human actions: a local SVM approach Christian Schüldt, Ivan Laptev, Barbara Caputo · Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. | 2004 | 3,011 |
| 46 | Privacy-preserving data mining Rakesh Agrawal, Ramakrishnan Srikant · ACM SIGMOD Record | 16 May 2000 | 2,990 |
| 47 | Distilling Free-Form Natural Laws from Experimental Data Michael H. Schmidt, Hod Lipson · Science | 2 Apr 2009 | 2,958 |
| 48 | Explainable AI: A Review of Machine Learning Interpretability Methods Pantelis Linardatos, Vasilis Papastefanopoulos, Sotiris B. Kotsiantis · Entropy | 25 Dec 2020 | 2,932 |
| 49 | Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning Takeru Miyato, Shin‐ichi Maeda, Masanori Koyama et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | 23 Jul 2018 | 2,897 |
| 50 | Deep learning and its applications to machine health monitoring Rui Zhao, Ruqiang Yan, Zhenghua Chen et al. · Mechanical Systems and Signal Processing | 14 Jun 2018 | 2,854 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.