# One-Class Classification: A Survey

arXiv: https://arxiv.org/abs/2101.03064 · HTML: https://mlsanity.com/paper/2101.03064/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 54 of its 102 references that are in the map.
- First posted on arXiv 8 Jan 2021.

## Builds on (most cited first, 30 of 54)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [Towards Open Set Deep Networks](https://mlsanity.com/paper/1511.06233/) (2015)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [Understanding Deep Image Representations by Inverting Them](https://mlsanity.com/paper/1412.0035/) (2014)
- [Return of Frustratingly Easy Domain Adaptation](https://mlsanity.com/paper/1511.05547/) (2015)
- [Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings](https://mlsanity.com/paper/1911.02357/) (2019)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Feature Denoising for Improving Adversarial Robustness](https://mlsanity.com/paper/1812.03411/) (2018)
- [Deep Semi-Supervised Anomaly Detection](https://mlsanity.com/paper/1906.02694/) (2019)
- [Deep Anomaly Detection Using Geometric Transformations](https://mlsanity.com/paper/1805.10917/) (2018)
- [Adversarially Learned One-Class Classifier for Novelty Detection](https://mlsanity.com/paper/1802.09088/) (2018)
- [OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations](https://mlsanity.com/paper/1903.08550/) (2019)

## Built on by (most cited first, 30 of 35)

- [A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges](https://mlsanity.com/paper/2110.14051/) (2021)
- [SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakes](https://mlsanity.com/paper/2211.11296/) (2022)
- [Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation](https://mlsanity.com/paper/2105.14737/) (2021)
- [Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems](https://mlsanity.com/paper/2305.14690/) (2023)
- [Universal Novelty Detection Through Adaptive Contrastive Learning](https://mlsanity.com/paper/2408.10798/) (2024)
- [PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomalies](https://mlsanity.com/paper/2506.09237/) (2025)
- [Probabilistic Robust Autoencoders for Outlier Detection](https://mlsanity.com/paper/2110.00494/) (2021)
- [Unveiling the Unseen: A Comprehensive Survey on Explainable Anomaly Detection in Images and Videos](https://mlsanity.com/paper/2302.06670/) (2023)
- [A Survey on Open-Set Image Recognition](https://mlsanity.com/paper/2312.15571/) (2023)
- [RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples](https://mlsanity.com/paper/2501.16971/) (2025)
- [Anomaly Detection using Edge Computing in Video Surveillance System: Review](https://mlsanity.com/paper/2107.02778/) (2021)
- [Data Invariants to Understand Unsupervised Out-of-Distribution Detection](https://mlsanity.com/paper/2111.13362/) (2021)
- [Creating an Atlas of Normal Tissue for Pruning WSI Patching Through Anomaly Detection](https://mlsanity.com/paper/2310.03106/) (2023)
- [Killing it with Zero-Shot: Adversarially Robust Novelty Detection](https://mlsanity.com/paper/2501.15271/) (2025)
- [Stochastic tensor space feature theory with applications to robust machine learning](https://mlsanity.com/paper/2110.01729/) (2021)
- [Active anomaly detection based on deep one-class classification](https://mlsanity.com/paper/2309.09465/) (2023)
- [Y-GAN: Learning Dual Data Representations for Efficient Anomaly Detection](https://mlsanity.com/paper/2109.14020/) (2021)
- [Forecast-based Multi-aspect Framework for Multivariate Time-series Anomaly Detection](https://mlsanity.com/paper/2201.04792/) (2022)
- [Learning The Likelihood Test With One-Class Classifiers for Physical Layer Authentication](https://mlsanity.com/paper/2210.12494/) (2022)
- [OOD-SEG: Exploiting out-of-distribution detection techniques for learning image segmentation from sparse multi-class positive-only annotations](https://mlsanity.com/paper/2411.09553/) (2024)
- [A Parameter-Efficient Quantum Anomaly Detection Method on a Superconducting Quantum Processor](https://mlsanity.com/paper/2412.16867/) (2024)
- [Adversarial Subspace Generation for Outlier Detection in High-Dimensional Data](https://mlsanity.com/paper/2504.07522/) (2025)
- [Pixel-wise Modulated Dice Loss for Medical Image Segmentation](https://mlsanity.com/paper/2506.15744/) (2025)
- [Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics](https://mlsanity.com/paper/2302.06048/) (2023)
- [AnomalousPatchCore: Exploring the Use of Anomalous Samples in Industrial Anomaly Detection](https://mlsanity.com/paper/2408.15113/) (2024)
- [ℓp Slack Norm Support Vector Data Description](https://mlsanity.com/paper/2203.08932/) (2022)
- [Semantic Novelty Detection and Characterization in Factual Text Involving Named Entities](https://mlsanity.com/paper/2210.17440/) (2022)
- [Adapting the Hypersphere Loss Function from Anomaly Detection to Anomaly Segmentation](https://mlsanity.com/paper/2301.09602/) (2023)
- [OCGEC: One-class Graph Embedding Classification for DNN Backdoor Detection](https://mlsanity.com/paper/2312.01585/) (2023)
- [Decentralised Traffic Incident Detection via Network Lasso](https://mlsanity.com/paper/2402.18167/) (2024)

## 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.