One-Class Classification

Also known as OCC

One-class classification learns what a target or normal class looks like, then flags examples that fall outside it—a useful approach when anomalies are rare or unlabeled. Methods include one-class support vector machines (OCSVMs), support vector data description (SVDD), deep representation learning, and density estimation on pretrained features. Applications include image and time-series anomaly detection, medical screening, fraud and security monitoring, and detecting forged or generated media. A central challenge is generalizing to unseen anomalies and shifts in normal data while keeping false alarms low and scores well calibrated.

Latest papers

24 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Geodesic-SVDD Latent Mixup for One-Shot Distributed Tabular Learning
Oct 24, 2026Andrea Moleri, Christian Internò, Markus Olhofer +2Federated Learning Tabular ML
Honda Research Institute Europe · University of Milan-Bicocca · Bielefeld University
TENET: Time-point Encoding Network for Multivariate Time Series Anomaly Detection
Oct 23, 2026Dongchan Cho, Juwon Hwang, Keumyeong Kang +2Unsupervised Anomaly Detection Time-Series Anomaly Detection
Industrial AI Lab, SimPlatform Co. Ltd. Affiliate Research Institute
FARE: Forensic Acceptance Region Estimation for Catching Bait-and-Switch Image Generators
Sep 25, 2026Kai Yao, Marc JuarezDigital Image Forensics One-Class Classification
School of Informatics The University of Edinburgh
Mapping melliferous tree species in Kenya via one-class classification with hyperspectral unsupervised domain adaptation
Aug 3, 2026Zhaozhi Luo, Janne Heiskanen, Ilja Vuorinne +6Unsupervised Domain Adaptation Tree Species Classification
Department of Geosciences and Geography, University of Helsinki, P.O. Box 64, 00014 Helsinki, Finland · Institute for Atmospheric and Earth System Research, University of Helsinki, P.O. Box 4, 00014 Helsinki, Finland · Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland +3
Deep Convolutional Large-Margin $\ell_p$ -SVDD for Visual Anomaly Detection
Jul 24, 2026Alireza Dastmalchi Saei, Shervin Rahimzadeh ArashlooKernel Methods One-Class Classification
Department of Computer Engineering, Bilkent University, 06800 Ankara, Türkiye
$μ$ Flow: Leveraging Average Images for Improving Generalisation of Deepfake Faces Detectors
Jun 29, 2026Orazio Pontorno, Mattia Litrico, Luca Guarnera +2Deepfake Detection One-Class Classification
University of Catania, Catania, Italy · University of Nottingham, Nottingham, UK
From CVE to CWE: Syscall-Based HIDS Generalisation
Jun 21, 2026Alexander V. Kozachok, Stanislav G. Vyugov, Shamil G. MagomedovCybersecurity One-Class Classification
MIREA – Russian Technological University, Moscow, Russian Federation · Academy of the Federal Guard Service of the Russian Federation, Oryol, Russian Federation
Sequential Minimal Optimization Algorithm for One-Class Support Vector Machines With Privileged Information
Jun 20, 2026Andrey Lange, Dmitry Smolyakov, Evgeny BurnaevSupport Vector Machines One-Class Classification
Skolkovo Institute of Science and Technology (Skoltech), Moscow 121205, Russia · Federal Research Center ‘‘Computer Science and Control" of Russian Academy of Sciences (FRC CSC RAS), Moscow 119333, Russia · Institute for Information Transmission Problems of Russian Academy of Sciences (IITP RAS), 127994 Moscow, Russia +1
Meta-classification of one-class classification models using ranking correlation and nearest neighbor
Jun 16, 2026Toshitaka Hayashi, Hamido Fujita, Dalibor Cimr +2Classification One-Class Classification
aFaculty of Science, University of Hradec Kralove, Hradecká 1285, Hradec Kralove, 50003, Czech Republic · bMalaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kampung Datuk Keramat, Kuala Lumpur, 54100, Malaysia · cRegional Research Center, Iwate Prefectural University, Sugo 152-52, Takizawa, 020-0693, Iwate, Japan
Decomposing one-class support vector machine into an ensemble of one-data support vector machines
Jun 14, 2026Toshitaka Hayashi, Dalibor Cimr, Hamido Fujita +1Support Vector Machines Classification
Faculty of Science, University of Hradec Kralove, Hradecká 1285, Hradec Kralove, 50003, Czech Republic · Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kampung Datuk Keramat, Kuala Lumpur, 54100, Malaysia · Regional Research Center, Iwate Prefectural University, Sugo 152-52, Takizawa, 020-0693, Iwate, Japan
DRIFT: From Robustness Gaps to Invariance Manifolds for AI-Generated Image Detection
Jun 5, 2026Abhishek Ameta, Sayan Banerjee, Shreyas Pandith +4Vision Foundation Model Adaptation One-Class Classification
Samsung Research Institute, Bangalore, India
VLAConf: Calibrated Task-Success Confidence for Vision-Language-Action Models
May 28, 2026Dehao Huang, Aoxiang Gu, Chengjie Zhang +5Confidence Estimation for VLMs Uncertainty Estimation for VLA Models
Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China. · Zhongguancun Academy, Beijing, China · National Cybersecurity Academy, Wuhan University, Wuhan, China.
CAMERA: Adapting to Semantic Camouflage in Unsupervised Text-Attributed Graph Fraud Detection
May 19, 2026Junjun Pan, Yixin Liu, Yu Zheng +3Text-Attributed Graph Learning Unsupervised Anomaly Detection
School of Information and Communication Technology, Griffith University, Australia · Department of Computer Science and Information Technology, La Trobe University, Australia
Privacy Policy Enforcement Guardrails for Data-Sensitive Retrieval-Augmented Generation
May 16, 2026Osama Zafar, Alexander Nemecek, Yiqian Zhang +5Data Leakage RAG Security
Dept. of Computer and Data Sciences Case Western Reserve University Cleveland, USA
Only Train Once: Uncertainty-Aware One-Class Learning for Face Authenticity Detection
May 11, 2026Qingchao Jiang, Zhenxuan Hou, Zhiying Zhu +3Image Forgery Detection Deepfake Detection
School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China · College of Computer Science and Artificial Intelligence, Fudan University, Shanghai 200433, China · Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore.
Learning from a single labeled face and a stream of unlabeled data
Apr 30, 2026Branislav Kveton, Michal ValkoOne-Class Classification Face Recognition
Technicolor Labs Palo Alto, CA · Inria Lille - Nord Europe, team SequeL Lille, France
VAE-Inf: A statistically interpretable generative paradigm for imbalanced classification
Apr 28, 2026Hongfei Wu, Ruijian Han, Yancheng YuanVariational Autoencoders Class-Imbalanced Learning
Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University · Department of Applied Mathematics The Hong Kong Polytechnic University
Towards Modality-Agnostic Medical Image Anomaly Detection: A Training-Free Manifold Refinement Approach
Apr 21, 2026Pritam Kar, Gouri Lakshmi S, Saptarshi BejMedical Image Anomaly Detection Manifold Learning
aSchool of Data Science, Indian Institute of Science Education and Research, Thiruvananthapuram, 695551, Kerala, India
Needle in a Haystack: One-Class Representation Learning for Detecting Rare Malignant Cells in Computational Cytology
Apr 9, 2026Swarnadip Chatterjee, Vladimir Basic, Arrigo Capitanio +2Whole-Slide Image Analysis Computational Pathology
Department of Information Technology, Uppsala University, Sweden · Department of Clinical Diagnostics, School of Health Sciences, Jönköping University, Sweden · Department of Pathology, Linköping University Hospital, Sweden
SRL-MAD: Structured Residual Latents for One-Class Morphing Attack Detection
Mar 16, 2026Diogo J. Paulo, Hugo Proença, João C. NevesImage Forgery Detection Face Presentation Attack Detection
University of Beira Interior, Portugal · IT: Instituto de Telecomunicações · NOVA LINCS
Distributed Convolutional Neural Networks for Object Recognition
Mar 10, 2026Liang SunConvolutional Neural Networks One-Class Classification
Shandong University of Science and Technology, Jinan Shandong province 250031, P.R. China
Neural quantum support vector data description for one-class classification
Mar 3, 2026Changjae Im, Hyeondo Oh, Daniel K. ParkOne-Class Classification Hybrid Quantum-Classical ML
Department of Statistics and Data Science, Yonsei University, Seoul, Republic of Korea · Department of Applied Statistics, Yonsei University, Seoul, Republic of Korea · Department of Quantum Information, Yonsei University, Seoul, Republic of Korea
SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images
Feb 23, 2026Aayush Dhakal, Subash Khanal, Srikumar Sastry +4AI-Generated Image Detection OOD Generalization
Washington University in St. Louis · Oak Ridge National Laboratory
Signature-Kernel One-Class Learning with Manifold Regularisation for Multivariate Time Series Anomaly Detection
Jul 31, 2025Shervin Rahimzadeh ArashlooTime-Series Anomaly Detection One-Class Classification
Department of Computer Engineering, Faculty of Engineering, Bilkent University, Ankara, Turkey.

Oct 24, 2026J/K move · Enter open · S save

Geodesic-SVDD Latent Mixup for One-Shot Distributed Tabular Learning

Andrea Moleri, Christian Internò, Markus Olhofer, Barbara Hammer+1

Honda Research Institute Europe · University of Milan-Bicocca · Bielefeld University

Federated Learning Tabular ML Mixup One-Class Classification Support Vector Machines

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Distributed Learning on structured data is a key and widely adopted approach in privacy-sensitive sectors, ranging from financial fraud prevention to federated healthcare diagnostics. While Gradient Boosted Decision Trees are widely used in centralized settings due to their efficiency, they are incompatible with standard gradient-based federated learning protocols because of their non-differentiability. Conversely, standard deep classifiers that are amenable to federated optimization often suffer from catastrophic forgetting under label distribution skew. In this work, to address these gaps we propose Geodesic-SVDD, a framework for Distributed Spherical Prototype Learning that recasts each client's task as one-class density estimation on the hypersphere, so that local models can be aggregated as probabilistic manifolds in a single round. Specifically, for each client, we employ a Residual Spherical Encoder and Geodesic Latent Mixup that enforces strict hyperspherical constraints (

\|φ(x)\| \equiv 1

), preventing the manifold collapse observed in linear approximations. Furthermore, leveraging this insight, we propose a novel One-Shot "Product of Experts" protocol that enables privacy-preserving aggregation without iterative synchronization. Finally, we mechanistically analyze the trade-off between interpolation strategies, demonstrating that while linear mixup offers an enticing computational shortcut, it destabilizes optimization gradients by orders of magnitude (

10^9\times

). Across five tabular benchmarks and four levels of label skew, Geodesic-SVDD attains the highest mean AUROC among seven federated methods, outperforms federated tree ensembles by up to 21 AUROC points on Covertype, and its One-Shot variant matches centralized Deep SVDD under IID and mild skew with a single communication round.