mlsanityData to 7 Oct 2026

Learning Deep Features for One-Class Classification

arXiv 1801.05365 ↗First posted 16 Jan 2018cs.CVcited by 76

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Abstract (arXiv)

We propose a deep learning-based solution for the problem of feature learning in one-class classification. The proposed method operates on top of a Convolutional Neural Network (CNN) of choice and produces descriptive features while maintaining a low intra-class variance in the feature space for the given class. For this purpose two loss functions, compactness loss and descriptiveness loss are proposed along with a parallel CNN architecture. A template matching-based framework is introduced to facilitate the testing process. Extensive experiments on publicly available anomaly detection, novelty detection and mobile active authentication datasets show that the proposed Deep One-Class (DOC) classification method achieves significant improvements over the state-of-the-art.

Key facts. Cited by 76 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Jan 2018.

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Citing papers per year: 2021: 30; 2022: 20; 2023: 15; 2024: 6; 2025: 5.

76 papers.

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  1. 2021VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localizationcited by 222
  2. 2022Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detectioncited by 104
  3. 2022Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detectioncited by 92
  4. 2021Mean-Shifted Contrastive Loss for Anomaly Detectioncited by 87
  5. 2022Training OOD Detectors in their Natural Habitatscited by 86
  6. 2022Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detectioncited by 79
  7. 2021Explainable Deep Few-shot Anomaly Detection with Deviation Networkscited by 69
  8. 2021Deep One-Class Classification via Interpolated Gaussian Descriptorcited by 67
  9. 2021Deep Graph-level Anomaly Detection by Glocal Knowledge Distillationcited by 67
  10. 2023ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstructioncited by 62
  11. 2021One-Class Classification: A Surveycited by 35
  12. 2021A Survey on Open Set Recognitioncited by 25
38 more
  1. 2022Perturbation Learning Based Anomaly Detectioncited by 24
  2. 2022Open-set Adversarial Defense with Clean-Adversarial Mutual Learningcited by 21
  3. 2021Dense outlier detection and open-set recognition based on training with noisy negative imagescited by 17
  4. 2022Anomaly Detection Requires Better Representationscited by 14
  5. 2023Set Features for Fine-grained Anomaly Detectioncited by 13
  6. 2024COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detectioncited by 13
  7. 2021Unsupervised Outlier Detection using Memory and Contrastive Learningcited by 11
  8. 2021Visual Anomaly Detection for Images: A Surveycited by 11
  9. 2022Learning image representations for anomaly detection: application to discovery of histological alterations in drug developmentcited by 11
  10. 2021Transfer Learning Gaussian Anomaly Detection by Fine-tuning Representationscited by 10
  11. 2023What can Discriminator do? Towards Box-free Ownership Verification of Generative Adversarial Networkcited by 10
  12. 2022Siamese Transition Masked Autoencoders as Uniform Unsupervised Visual Anomaly Detectorcited by 9
  13. 2021Self-Supervised Features Improve Open-World Learningcited by 8
  14. 2021Can self-training identify suspicious ugly duckling lesions?cited by 7
  15. 2021Physics-Guided Generative Adversarial Networks for Sea Subsurface Temperature Predictioncited by 7
  16. 2023No Free Lunch: The Hazards of Over-Expressive Representations in Anomaly Detectioncited by 7
  17. 2021Self-supervised Representation Learning for Reliable Robotic Monitoring of Fruit Anomaliescited by 6
  18. 2021Out-of-Distribution Detection Without Class Labelscited by 4
  19. 2022Data refinement for fully unsupervised visual inspection using pre-trained networkscited by 4
  20. 2023Why do Angular Margin Losses work well for Semi-Supervised Anomalous Sound Detection?cited by 4
  21. 2021Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspectioncited by 3
  22. 2021Latent-Insensitive autoencoders for Anomaly Detectioncited by 3
  23. 2021Anomaly Detection in IR Images of PV Modules using Supervised Contrastive Learningcited by 3
  24. 2022Self-Calibrating Anomaly and Change Detection for Autonomous Inspection Robotscited by 3
  25. 2023Set Features for Anomaly Detectioncited by 3
  26. 2025GAL-MAD: Towards Explainable Anomaly Detection in Microservice Applications Using Graph Attention Networkscited by 3
  27. 2021Margin-Aware Intra-Class Novelty Identification for Medical Imagescited by 2
  28. 2021Y-GAN: Learning Dual Data Representations for Efficient Anomaly Detectioncited by 2
  29. 2022Forecast-based Multi-aspect Framework for Multivariate Time-series Anomaly Detectioncited by 2
  30. 2022Unknown Face Presentation Attack Detection via Localised Learning of Multiple Kernelscited by 2
  31. 2022Red PANDA: Disambiguating Anomaly Detection by Removing Nuisance Factorscited by 2
  32. 2022TrADe Re-ID -- Live Person Re-Identification using Tracking and Anomaly Detectioncited by 2
  33. 2022Weakly Supervised Video Anomaly Detection Based on Cross-Batch Clustering Guidancecited by 2
  34. 2023An Efficient Ensemble Explainable AI (XAI) Approach for Morphed Face Detectioncited by 2
  35. 2023SaliencyCut: Augmenting Plausible Anomalies for Anomaly Detectioncited by 2
  36. 2023Restricted Generative Projection for One-Class Classification and Anomaly Detectioncited by 2
  37. 2023Multi-representations Space Separation based Graph-level Anomaly-aware Detectioncited by 2
  38. 2024Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignmentcited by 2

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