mlsanityData to 7 Oct 2026

PyOD: A Python Toolbox for Scalable Outlier Detection

arXiv 1901.01588 ↗First posted 6 Jan 2019cs.LG cs.IR stat.MLcited by 221

Not on the map. Why?

Abstract (arXiv)

PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data. Uniquely, it provides access to a wide range of outlier detection algorithms, including established outlier ensembles and more recent neural network-based approaches, under a single, well-documented API designed for use by both practitioners and researchers. With robustness and scalability in mind, best practices such as unit testing, continuous integration, code coverage, maintainability checks, interactive examples and parallelization are emphasized as core components in the toolbox's development. PyOD is compatible with both Python 2 and 3 and can be installed through Python Package Index (PyPI) or https://github.com/yzhao062/pyod.

Key facts. Cited by 221 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jan 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 27; 2022: 42; 2023: 48; 2024: 33; 2025: 40; 2026: 31.

221 papers.

Sort:

By area:

  1. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  2. 2022ADBench: Anomaly Detection Benchmarkcited by 230
  3. 2022ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functionscited by 153
  4. 2022Deep Isolation Forest for Anomaly Detectioncited by 90
  5. 2022BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphscited by 80
  6. 2021Sample Selection with Uncertainty of Losses for Learning with Noisy Labelscited by 79
  7. 2022Generating High Fidelity Data from Low-density Regions using Diffusion Modelscited by 78
  8. 2022Anomalib: A Deep Learning Library for Anomaly Detectioncited by 75
  9. 2021An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Seriescited by 68
  10. 2023GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detectioncited by 57
  11. 2021LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networkscited by 42
  12. 2023Don't Play Favorites: Minority Guidance for Diffusion Modelscited by 37
38 more
  1. 2022PyGOD: A Python Library for Graph Outlier Detectioncited by 31
  2. 2023Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learningcited by 25
  3. 2024AD-LLM: Benchmarking Large Language Models for Anomaly Detectioncited by 25
  4. 2021An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinerycited by 22
  5. 2023Weakly Supervised Anomaly Detection: A Surveycited by 22
  6. 2023Unsupervised anomaly detection algorithms on real-world data: how many do we need?cited by 20
  7. 2021Deep Clustering based Fair Outlier Detectioncited by 19
  8. 2024Self-Guided Generation of Minority Samples Using Diffusion Modelscited by 19
  9. 2022Toward Unsupervised Outlier Model Selectioncited by 18
  10. 2024NLP-ADBench: NLP Anomaly Detection Benchmarkcited by 17
  11. 2024PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selectioncited by 17
  12. 2024MetaOOD: Automatic Selection of OOD Detection Modelscited by 16
  13. 2022ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labelscited by 15
  14. 2022DORA: Exploring Outlier Representations in Deep Neural Networkscited by 14
  15. 2022Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detectioncited by 14
  16. 2023NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generationcited by 14
  17. 2022Towards Similarity-Aware Time-Series Classificationcited by 13
  18. 2021Conformal Anomaly Detection on Spatio-Temporal Observations with Missing Datacited by 12
  19. 2021Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active Learningcited by 11
  20. 2021Differential Anomaly Detection for Facial Imagescited by 11
  21. 2022Fast Nonlinear Vector Quantile Regressioncited by 11
  22. 2022AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detectioncited by 11
  23. 2023RoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous Supervisioncited by 11
  24. 2023Open-Set Likelihood Maximization for Few-Shot Learningcited by 10
  25. 2023Explainable Contextual Anomaly Detection using Quantile Regression Forestscited by 10
  26. 2023Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networkscited by 10
  27. 2022On Out-of-Distribution Detection for Audio with Deep Nearest Neighborscited by 9
  28. 2023Unsupervised Anomaly Detection with Rejectioncited by 9
  29. 2025ClimateLLM: Efficient Weather Forecasting via Frequency-Aware Large Language Modelscited by 9
  30. 2021A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?cited by 8
  31. 2021Affine-Invariant Integrated Rank-Weighted Depth: Definition, Properties and Finite Sample Analysiscited by 8
  32. 2023Graph Neural Networks based Log Anomaly Detection and Explanationcited by 8
  33. 2023METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detectioncited by 8
  34. 2024GPT-generated Text Detection: Benchmark Dataset and Tensor-based Detection Methodcited by 8
  35. 2026From Zero to Hero: Advancing Zero-Shot Foundation Models for Tabular Outlier Detectioncited by 8
  36. 2021Probabilistic Robust Autoencoders for Outlier Detectioncited by 7
  37. 2022Mul-GAD: a semi-supervised graph anomaly detection framework via aggregating multi-view informationcited by 7
  38. 2023UADB: Unsupervised Anomaly Detection Boostercited by 7

50 most cited of 221 All 221 (JSON)