One-class classifier

Benchmarks archive 2025-07-28

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Datasets archive 2025-07-28

3 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

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Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

26 shown of 26 papers with code (82 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Adversarially Learned One-Class Classifier for Novelty Detection
  25 Feb 2018 5 repositories listed
  Our architecture is composed of two deep networks, each of which trained by competing with each other while collaborating to understand the underlying concept in the target class, and then classify the testing samples.
• Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networks
  22 Jul 2020 3 repositories listed
  The proposed system is evaluated on the publicly available WMCA multi-channel face PAD database, which contains a wide variety of 2D and 3D attacks.
• Near out-of-distribution detection for low-resolution radar micro-Doppler signatures
  12 May 2022 2 repositories listed
  We emphasize the relevance of OODD and its specific supervision requirements for the detection of a multimodal, diverse targets class among other similar radar targets and clutter in real-life critical systems.
• CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
  8 Apr 2021 2 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 2 pointer-only (licence)
  We aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data.
• Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output
  23 Oct 2019 2 repositories listed
  Several approaches have been proposed to detect OOD inputs, but the detection task is still an ongoing challenge.
• FedMSE: Federated learning for IoT network intrusion detection
  18 Oct 2024 1 repository listed
  This approach enhances the performance of intrusion detection by effectively representing normal network data and accurately identifying anomalies in the decentralized strategy.
• Generative Semi-supervised Graph Anomaly Detection
  19 Feb 2024 1 repository listed Syntology ran 11 of 19 samples · 8 unverified · 14 pointer-only (licence)
  This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal, contrasting to the extensively explored unsupervised setting with a…
• OCGEC: One-class Graph Embedding Classification for DNN Backdoor Detection
  4 Dec 2023 1 repository listed
  We then pre-train a generative self-supervised graph autoencoder (GAE) to better learn the features of benign models in order to detect backdoor models without knowing the attack strategy.
• UNTAG: LEARNING GENERIC FEATURES FOR UNSUPERVISED TYPE-AGNOSTIC DEEPFAKE DETECTION
  4 Jun 2023 1 repository listed
  This paper introduces a novel framework for unsupervised type-agnostic deepfake detection called UNTAG.
• One-Class Risk Estimation for One-Class Hyperspectral Image Classification
  27 Oct 2022 1 repository listed
  Hyperspectral imagery (HSI) one-class classification is aimed at identifying a single target class from the HSI by using only knowing positive data, which can significantly reduce the requirements for annotation.
• Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection
  25 Jul 2022 1 repository listed Syntology ran 0 of 14 samples · 14 unverified
  To tackle these problems, this paper proposes calibrated one-class classification for anomaly detection, realizing contamination-tolerant, anomaly-informed learning of data normality via uncertainty modeling-based…
• SIFT and SURF based feature extraction for the anomaly detection
  24 Mar 2022 1 repository listed
  In this paper, we suggest a way, how to use SIFT and SURF algorithms to extract the image features for anomaly detection.
• Exemplar-free Class Incremental Learning via Discriminative and Comparable One-class Classifiers
  5 Jan 2022 1 repository listed
  DisCOIL follows the basic principle of POC, but it adopts variational auto-encoders (VAE) instead of other well-established one-class classifiers (e.
• Shell Theory: A Statistical Model of Reality
  28 May 2021 1 repository listed
  The foundational assumption of machine learning is that the data under consideration is separable into classes; while intuitively reasonable, separability constraints have proven remarkably difficult to formulate…
• Learning and Evaluating Representations for Deep One-class Classification
  4 Nov 2020 1 repository listed
  We first learn self-supervised representations from one-class data, and then build one-class classifiers on learned representations.
• ARCADe: A Rapid Continual Anomaly Detector
  10 Aug 2020 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Although continual learning and anomaly detection have separately been well-studied in previous works, their intersection remains rather unexplored.
• Quantum One-class Classification With a Distance-based Classifier
  31 Jul 2020 1 repository listed
  We present a new classifier based on HC named Quantum One-class Classifier (QOCC) that consists of a minimal quantum machine learning model with fewer operations and qubits, thus being able to mitigate errors from NISQ…
• Few-Shot One-Class Classification via Meta-Learning
  8 Jul 2020 1 repository listed
  Our experiments on eight datasets from the image and time-series domains show that our method leads to better results than classical OCC and few-shot classification approaches, and demonstrate the ability to learn…
• COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature
  11 Jun 2020 1 repository listed
  (1) We show that COVID-19-CT-CXR, when used as additional training data, is able to contribute to improved DL performance for the classification of COVID-19 and non-COVID-19 CT.
• Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm
  16 Apr 2020 1 repository listed Syntology ran 0 of 3 samples · 3 unverified
  Another possible approach is to use both generator and discriminator for anomaly detection.
• Dynamic Decision Boundary for One-class Classifiers applied to non-uniformly Sampled Data
  5 Apr 2020 1 repository listed
  A typical issue in Pattern Recognition is the non-uniformly sampled data, which modifies the general performance and capability of machine learning algorithms to make accurate predictions.
• Feature Learning for Fault Detection in High-Dimensional Condition-Monitoring Signals
  12 Oct 2018 1 repository listed
  The performance is first evaluated on a synthetic dataset that encompasses typical characteristics of condition monitoring data.
• Generative Probabilistic Novelty Detection with Adversarial Autoencoders
  6 Jul 2018 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  We assume that training data is available to describe only the inlier distribution.
• Localized Multiple Kernel Learning for Anomaly Detection: One-class Classification
  21 May 2018 1 repository listed
  In this paper, we present a multiple kernel learning approach for the One-class Classification (OCC) task and employ it for anomaly detection.
• Satellite Image Forgery Detection and Localization Using GAN and One-Class Classifier
  13 Feb 2018 1 repository listed
  Specifically, we consider the scenario in which pixels within a region of a satellite image are replaced to add or remove an object from the scene.
• Automatic support vector data description
  1 Jan 2018 1 repository listed
  Event handlers have wide range of applications such as medical assistant systems and fire suppression systems.

Syntology lines on 6 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.