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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 11–20 of 227 papers

TitleStatusHype
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
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