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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 81–90 of 227 papers

TitleStatusHype
ProtoFL: Unsupervised Federated Learning via Prototypical Distillation—0
Image Outlier Detection Without Training using RANSACCode0
LBL: Logarithmic Barrier Loss Function for One-class ClassificationCode0
Restricted Generative Projection for One-Class Classification and Anomaly Detection—0
UNTAG: LEARNING GENERIC FEATURES FOR UNSUPERVISED TYPE-AGNOSTIC DEEPFAKE DETECTIONCode0
Learning Off-Road Terrain Traversability with Self-Supervisions Only—0
Wooden Sleeper Deterioration Detection for Rural Railway Prognostics Using Unsupervised Deeper FCDDs—0
SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classification—0
Hyperspectral Image Analysis with Subspace Learning-based One-Class Classification—0
Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection—0
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