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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 21–30 of 227 papers

TitleStatusHype
Synthetic Temporal Anomaly Guided End-to-End Video Anomaly DetectionCode1
Deep One-Class Classification via Interpolated Gaussian DescriptorCode1
MOCCA: Multi-Layer One-Class ClassificAtion for Anomaly DetectionCode1
Learning and Evaluating Representations for Deep One-class ClassificationCode1
PANDA: Adapting Pretrained Features for Anomaly Detection and SegmentationCode1
Meta Learning for Few-Shot One-class ClassificationCode1
Few-Shot One-Class Classification via Meta-LearningCode1
Explainable Deep One-Class ClassificationCode1
Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly DetectionCode1
One-Class Graph Neural Networks for Anomaly Detection in Attributed NetworksCode1
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