Critical Review for One-class Classification: recent advances and the reality behind them
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Abstract (arXiv)
This paper offers a comprehensive review of one-class classification (OCC), examining the technologies and methodologies employed in its implementation. It delves into various approaches utilized for OCC across diverse data types, such as feature data, image, video, time series, and others. Through a systematic review, this paper synthesizes promi-nent strategies used in OCC from its inception to its current advance-ments, with a particular emphasis on the promising application. Moreo-ver, the article criticizes the state-of-the-art (SOTA) image anomaly de-tection (AD) algorithms dominating one-class experiments. These algo-rithms include outlier exposure (binary classification) and pretrained model (multi-class classification), conflicting with the fundamental con-cept of learning from one class. Our investigation reveals that the top nine algorithms for one-class CIFAR10 benchmark are not OCC. We ar-gue that binary/multi-class classification algorithms should not be com-pared with OCC.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 43 of its 147 references that are in the map. First posted on arXiv 27 Apr 2024.
Builds on
43 of 147 references.
References · 43
- 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,591
- 2016Variational Graph Auto-Encoderscited by 1,382
- 2018Deep Anomaly Detection with Outlier Exposurecited by 942
- 2019Using Self-Supervised Learning Can Improve Model Robustness and Uncertaintycited by 514
- 2020CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instancescited by 441
- 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 425
- 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 421
- 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
- 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
- 2018Deep Anomaly Detection Using Geometric Transformationscited by 269
- 2021SSD: A Unified Framework for Self-Supervised Outlier Detectioncited by 264
- 2019PyOD: A Python Toolbox for Scalable Outlier Detectioncited by 222
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- 2018Learning from positive and unlabeled data: a surveycited by 219
- 2020Classification-Based Anomaly Detection for General Datacited by 211
- 2019OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representationscited by 199
- 2016Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learningcited by 195
- 2021Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learningcited by 177
- 2020PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentationcited by 175
- 2020Deep Learning for Anomaly Detection: A Reviewcited by 159
- 2020Learning and Evaluating Representations for Deep One-class Classificationcited by 133
- 2020Explainable Deep One-Class Classificationcited by 127
- 2018Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifierscited by 126
- 2021Pre-training without Natural Imagescited by 100
- 2022Deep Isolation Forest for Anomaly Detectioncited by 92
- 2021Mean-Shifted Contrastive Loss for Anomaly Detectioncited by 88
- 2018Extended Isolation Forestcited by 81
- 2020A Survey on Text Classification: From Shallow to Deep Learningcited by 80
- 2013One-Class Classification: Taxonomy of Study and Review of Techniquescited by 68
- 2021Deep One-Class Classification via Interpolated Gaussian Descriptorcited by 67
- 2020TODS: An Automated Time Series Outlier Detection Systemcited by 41
- 2019One-Class Convolutional Neural Networkcited by 37
- 2022Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Imagescited by 35
- 2022PyGOD: A Python Library for Graph Outlier Detectioncited by 32
- 2018Outlier Aware Network Embedding for Attributed Networkscited by 29
- 2022AnoViT: Unsupervised Anomaly Detection and Localization with Vision Transformer-based Encoder-Decodercited by 29
- 2020ARAE: Adversarially Robust Training of Autoencoders Improves Novelty Detectioncited by 27
- 2021Transformaly -- Two (Feature Spaces) Are Better Than Onecited by 25
- 2019Multimodal Subspace Support Vector Data Descriptioncited by 15
- 2022Anomaly Detection Requires Better Representationscited by 15
- 2021Unsupervised Two-Stage Anomaly Detectioncited by 12
- 2021Average Localised Proximity: A new data descriptor with good default one-class classification performancecited by 7
- 2020Deep Unsupervised Image Anomaly Detection: An Information Theoretic Frameworkcited by 6
- 2021Constrained Adaptive Projection with Pretrained Features for Anomaly Detectioncited by 4
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