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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 1–25 of 227 papers

TitleStatusHype
MOCCA: Multi-Layer One-Class ClassificAtion for Anomaly DetectionCode1
One-Class Convolutional Neural NetworkCode1
Inter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class ClassificationCode1
Meta Learning for Few-Shot One-class ClassificationCode1
Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly DetectionCode1
One-Class Graph Neural Networks for Anomaly Detection in Attributed NetworksCode1
Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly DetectionCode1
Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly DetectionCode1
Learning and Evaluating Representations for Deep One-class ClassificationCode1
One-Class Risk Estimation for One-Class Hyperspectral Image ClassificationCode1
FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training DataCode1
Which Model Generated This Image? A Model-Agnostic Approach for Origin AttributionCode1
Explainable Deep One-Class ClassificationCode1
Few-Shot One-Class Classification via Meta-LearningCode1
A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly DetectionCode1
Deep Contrastive One-Class Time Series Anomaly DetectionCode1
Anomaly Detection in Emails using Machine Learning and Header InformationCode1
Calibrated One-class Classification for Unsupervised Time Series Anomaly DetectionCode1
Label-Free Multivariate Time Series Anomaly DetectionCode1
Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel DescriptorsCode1
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly DetectionCode1
Learning Not to Reconstruct AnomaliesCode1
Dual-Distribution Discrepancy for Anomaly Detection in Chest X-RaysCode1
Diversity-Measurable Anomaly DetectionCode1
PANDA: Adapting Pretrained Features for Anomaly Detection and SegmentationCode1
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