mlsanityData to 7 Oct 2026

Anomaly Detection using One-Class Neural Networks

arXiv 1802.06360 ↗First posted 18 Feb 2018cs.LG cs.NE stat.MLcited by 82

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Abstract (arXiv)

We propose a one-class neural network (OC-NN) model to detect anomalies in complex data sets. OC-NN combines the ability of deep networks to extract a progressively rich representation of data with the one-class objective of creating a tight envelope around normal data. The OC-NN approach breaks new ground for the following crucial reason: data representation in the hidden layer is driven by the OC-NN objective and is thus customized for anomaly detection. This is a departure from other approaches which use a hybrid approach of learning deep features using an autoencoder and then feeding the features into a separate anomaly detection method like one-class SVM (OC-SVM). The hybrid OC-SVM approach is sub-optimal because it is unable to influence representational learning in the hidden layers. A comprehensive set of experiments demonstrate that on complex data sets (like CIFAR and GTSRB), OC-NN performs on par with state-of-the-art methods and outperformed conventional shallow methods in some scenarios.

Key facts. Cited by 82 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Feb 2018.

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Citing papers per year: 2021: 26; 2022: 19; 2023: 22; 2024: 5; 2025: 7; 2026: 3.

82 papers.

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  1. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 510
  2. 2021SSD: A Unified Framework for Self-Supervised Outlier Detectioncited by 264
  3. 2021VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localizationcited by 222
  4. 2021Student-Teacher Feature Pyramid Matching for Anomaly Detectioncited by 145
  5. 2022Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detectioncited by 104
  6. 2022Training OOD Detectors in their Natural Habitatscited by 86
  7. 2021Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detectioncited by 83
  8. 2021Explainable Deep Few-shot Anomaly Detection with Deviation Networkscited by 69
  9. 2021Reconstruction Student with Attention for Student-Teacher Pyramid Matchingcited by 20
  10. 2022Visual Anomaly Detection Via Partition Memory Bank Module and Error Estimationcited by 20
  11. 2021Do We Really Need to Learn Representations from In-domain Data for Outlier Detection?cited by 16
  12. 2021Out-of-Distribution Dynamics Detection: RL-Relevant Benchmarks and Resultscited by 14
38 more
  1. 2024Looking 3D: Anomaly Detection with 2D-3D Alignmentcited by 13
  2. 2021MemStream: Memory-Based Streaming Anomaly Detectioncited by 12
  3. 2022Task-oriented Self-supervised Learning for Anomaly Detection in Electroencephalographycited by 11
  4. 2021Context-Dependent Anomaly Detection for Low Altitude Traffic Surveillancecited by 10
  5. 2022Deep Contrastive One-Class Time Series Anomaly Detectioncited by 10
  6. 2022Task Agnostic and Post-hoc Unseen Distribution Detectioncited by 8
  7. 2023Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptorscited by 8
  8. 2023Anomaly Detection with Variance Stabilized Density Estimationcited by 8
  9. 2025Human Texts Are Outliers: Detecting LLM-generated Texts via Out-of-distribution Detectioncited by 8
  10. 2021Efficient Anomaly Detection Using Self-Supervised Multi-Cue Taskscited by 6
  11. 2023Learning Representation for Anomaly Detection of Vehicle Trajectoriescited by 6
  12. 2021Data Invariants to Understand Unsupervised Out-of-Distribution Detectioncited by 5
  13. 2022ADPS: Asymmetric Distillation Post-Segmentation for Image Anomaly Detectioncited by 5
  14. 2022FRE: A Fast Method For Anomaly Detection And Segmentationcited by 5
  15. 2023One-class Damage Detector Using Deeper Fully-Convolutional Data Descriptions for Civil Applicationcited by 5
  16. 2022ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variablescited by 4
  17. 2023An Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contaminationcited by 4
  18. 2023SeMAnD: Self-Supervised Anomaly Detection in Multimodal Geospatial Datasetscited by 4
  19. 2024Odd-One-Out: Anomaly Detection by Comparing with Neighborscited by 4
  20. 2021Understanding the Role of Self-Supervised Learning in Out-of-Distribution Detection Taskcited by 3
  21. 2023DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series Datacited by 3
  22. 2023Active anomaly detection based on deep one-class classificationcited by 3
  23. 2023Human-centric Behavior Description in Videos: New Benchmark and Modelcited by 3
  24. 2023Tight Rates in Supervised Outlier Transfer Learningcited by 3
  25. 2022Unknown Face Presentation Attack Detection via Localised Learning of Multiple Kernelscited by 2
  26. 2022Meta-learning for Out-of-Distribution Detection via Density Estimation in Latent Spacecited by 2
  27. 2022R2-AD2: Detecting Anomalies by Analysing the Raw Gradientcited by 2
  28. 2022InQMAD: Incremental Quantum Measurement Anomaly Detectioncited by 2
  29. 2023SaliencyCut: Augmenting Plausible Anomalies for Anomaly Detectioncited by 2
  30. 2023Anomaly detection with semi-supervised classification based on risk estimatorscited by 2
  31. 2021The Deep Radial Basis Function Data Descriptor (D-RBFDD) Network: A One-Class Neural Network for Anomaly Detectioncited by 1
  32. 2021Anomaly Detection Based on Selection and Weighting in Latent Spacecited by 1
  33. 2021Cyber Intrusion Detection by Using Deep Neural Networks with Attack-sharing Losscited by 1
  34. 2021Multi-Perspective Anomaly Detectioncited by 1
  35. 2021Outlier Detection using AI: A Surveycited by 1
  36. 2022Scrutinizing Shipment Records To Thwart Illegal Timber Tradecited by 1
  37. 2022Connective Reconstruction-based Novelty Detectioncited by 1
  38. 2023Unsupervised Ensemble Methods for Anomaly Detection in PLC-based Process Controlcited by 1

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