mlsanityData to 7 Oct 2026

One-Class Classification: A Survey

arXiv 2101.03064 ↗First posted 8 Jan 2021cs.CV cs.LGcited by 35

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Abstract (arXiv)

One-Class Classification (OCC) is a special case of multi-class classification, where data observed during training is from a single positive class. The goal of OCC is to learn a representation and/or a classifier that enables recognition of positively labeled queries during inference. This topic has received considerable amount of interest in the computer vision, machine learning and biometrics communities in recent years. In this article, we provide a survey of classical statistical and recent deep learning-based OCC methods for visual recognition. We discuss the merits and drawbacks of existing OCC approaches and identify promising avenues for research in this field. In addition, we present a discussion of commonly used datasets and evaluation metrics for OCC.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). Builds on 54 of its 102 references that are in the map. First posted on arXiv 8 Jan 2021.

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54 of 102 references.

References · 54

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  17. 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
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  22. 2020Explainable Deep One-Class Classificationcited by 126
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 5; 2023: 8; 2024: 6; 2025: 5; 2026: 4.

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  2. 2022SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakescited by 49
  3. 2021Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentationcited by 26
  4. 2023Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problemscited by 9
  5. 2024Universal Novelty Detection Through Adaptive Contrastive Learningcited by 9
  6. 2025PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomaliescited by 8
  7. 2021Probabilistic Robust Autoencoders for Outlier Detectioncited by 7
  8. 2023Unveiling the Unseen: A Comprehensive Survey on Explainable Anomaly Detection in Images and Videoscited by 7
  9. 2023A Survey on Open-Set Image Recognitioncited by 7
  10. 2025RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samplescited by 7
  11. 2021Anomaly Detection using Edge Computing in Video Surveillance System: Reviewcited by 6
  12. 2021Data Invariants to Understand Unsupervised Out-of-Distribution Detectioncited by 5
23 more
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  2. 2025Killing it with Zero-Shot: Adversarially Robust Novelty Detectioncited by 4
  3. 2021Stochastic tensor space feature theory with applications to robust machine learningcited by 3
  4. 2023Active anomaly detection based on deep one-class classificationcited by 3
  5. 2021Y-GAN: Learning Dual Data Representations for Efficient Anomaly Detectioncited by 2
  6. 2022Forecast-based Multi-aspect Framework for Multivariate Time-series Anomaly Detectioncited by 2
  7. 2022Learning The Likelihood Test With One-Class Classifiers for Physical Layer Authenticationcited by 2
  8. 2024OOD-SEG: Exploiting out-of-distribution detection techniques for learning image segmentation from sparse multi-class positive-only annotationscited by 2
  9. 2024A Parameter-Efficient Quantum Anomaly Detection Method on a Superconducting Quantum Processorcited by 2
  10. 2025Pixel-wise Modulated Dice Loss for Medical Image Segmentationcited by 2
  11. 2023Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamicscited by 1
  12. 2024AnomalousPatchCore: Exploring the Use of Anomalous Samples in Industrial Anomaly Detectioncited by 1
  13. 2025Adversarial Subspace Generation for Outlier Detection in High-Dimensional Datacited by 1
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  17. 2023OCGEC: One-class Graph Embedding Classification for DNN Backdoor Detectioncited by 0
  18. 2024Decentralised Traffic Incident Detection via Network Lassocited by 0
  19. 2024Redefining Normal: A Novel Object-Level Approach for Multi-Object Novelty Detectioncited by 0
  20. 2026Neural quantum support vector data description for one-class classificationcited by 0
  21. 2026Needle in a Haystack: One-Class Representation Learning for Detecting Rare Malignant Cells in Computational Cytologycited by 0
  22. 2026Better Protein Function Prediction by Modeling Survivorship Biascited by 0
  23. 2026Local Sparsity Enables Unsupervised LLM Safety Detectioncited by 0