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One-Class Classification

One-class classification (OCC) algorithms serve a crucial role in scenarios where the negative class is either absent, poorly sampled, or not well defined. This unique situation presents a challenge for building effective classifiers, as they must delineate the class boundary solely based on knowledge of the positive class. OCC has found application in various research domains, including outlier/novelty detection and concept learning.

In the context of anomaly detection, OCC models are trained exclusively on "normal" data and are subsequently tasked with identifying anomalous patterns during inference.

A one-class classifier aims at capturing characteristics of training instances, in order to be able to distinguish between them and potential outliers to appear.

— Page 139, Learning from Imbalanced Data Sets, 2018.

Papers

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Showing 1–50 of 227 papers

TitleStatusHype
SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Images—0
Adversarial Subspace Generation for Outlier Detection in High-Dimensional DataCode0
RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated DataCode0
From Pixels to Trajectory: Universal Adversarial Example Detection via Temporal Imprints—0
FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detection—0
Anomaly Detection in Smart Power Grids with Graph-Regularized MS-SVDD: a Multimodal Subspace Learning Approach—0
One Class Restricted Kernel MachinesCode0
Score Combining for Contrastive OOD Detection—0
Teacher Encoder-Student Decoder Denoising Guided Segmentation Network for Anomaly Detection—0
On the Adversarial Robustness of Benjamini Hochberg—0
Beyond Generation: A Diffusion-based Low-level Feature Extractor for Detecting AI-generated Images—0
Task-Specific Gradient Adaptation for Few-Shot One-Class Classification—0
MEATRD: Multimodal Anomalous Tissue Region Detection Enhanced with Spatial TranscriptomicsCode0
FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training DataCode1
Disentangling Tabular Data Towards Better One-Class Anomaly DetectionCode0
Point Cloud Novelty Detection Based on Latent Representations of a General Feature Extractor—0
On The Relationship between Visual Anomaly-free and Anomalous Representations—0
Linear-time One-Class Classification with Repeated Element-wise FoldingCode0
Non-Robust Features are Not Always Useful in One-Class Classification—0
Quality assurance of organs-at-risk delineation in radiotherapy—0
One-Class Classification as GLRT for Jamming Detection in Private 5G Networks—0
Critical Review for One-class Classification: recent advances and the reality behind them—0
Hyp-OC: Hyperbolic One Class Classification for Face Anti-Spoofing—0
Which Model Generated This Image? A Model-Agnostic Approach for Origin AttributionCode1
PREGO: online mistake detection in PRocedural EGOcentric videosCode1
LLM meets Vision-Language Models for Zero-Shot One-Class Classification—0
A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats—0
usfAD Based Effective Unknown Attack Detection Focused IDS Framework—0
Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite Kernel Strategy in One-Class Classification—0
Understanding Time Series Anomaly State Detection through One-Class Classification—0
Trustworthiness of X Users: A One-Class Classification Approach—0
Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly DetectionCode1
Lp-Norm Constrained One-Class Classifier Combination—0
Advancing Image Retrieval with Few-Shot Learning and Relevance FeedbackCode0
Label-Free Multivariate Time Series Anomaly DetectionCode1
Learning Polynomial Representations of Physical Objects with Application to Certifying Correct Packing Configurations—0
OCGEC: One-class Graph Embedding Classification for DNN Backdoor DetectionCode0
Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach—0
Enhancing Sentiment Analysis Results through Outlier Detection Optimization—0
Interpretable pap smear cell representation for cervical cancer screening—0
A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly DetectionCode1
Deep Learning Predicts Biomarker Status and Discovers Related Histomorphology Characteristics for Low-Grade Glioma—0
Efficient Training of One Class Classification-SVMs—0
Credit Card Fraud Detection with Subspace Learning-based One-Class Classification—0
Newton Method-based Subspace Support Vector Data Description—0
One-Class Classification for Intrusion Detection on Vehicular Networks—0
Convolutional autoencoder-based multimodal one-class classification—0
Active anomaly detection based on deep one-class classification—0
An Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contamination—0
A Perceptron-based Fine Approximation Technique for Linear Separation—0
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