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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 111–120 of 227 papers

TitleStatusHype
Conical Classification For Computationally Efficient One-Class Topic Determination—0
Synthetic Temporal Anomaly Guided End-to-End Video Anomaly DetectionCode1
Learning Not to Reconstruct AnomaliesCode1
Impact of Channel Variation on One-Class Learning for Spoof DetectionCode0
Unsupervised Artifact Detection for Whole Slide Images of Prostate Biopsies—0
Conceptron: a probabilistic deep one-class classification method—0
FROB: Few-shot ROBust Model for Classification with Out-of-Distribution Detection—0
Deep Inverse Reinforcement Learning via Adversarial One-Class Classification—0
One-Class Meta-Learning: Towards Generalizable Few-Shot Open-Set Classification—0
One-Class Classification for Wafer Map using Adversarial Autoencoder with DSVDD Prior—0
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