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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 126–150 of 227 papers

TitleStatusHype
SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classification—0
Statistical and Machine Learning-based Decision Techniques for Physical Layer Authentication—0
STEP-GAN: A Step-by-Step Training for Multi Generator GANs with application to Cyber Security in Power Systems—0
Support Spinor Machine—0
Synthetic Pseudo Anomalies for Unsupervised Video Anomaly Detection: A Simple yet Efficient Framework based on Masked Autoencoder—0
Task-Specific Gradient Adaptation for Few-Shot One-Class Classification—0
Teacher Encoder-Student Decoder Denoising Guided Segmentation Network for Anomaly Detection—0
Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network—0
Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection—0
Towards Anomaly Detection in Dashcam Videos—0
Towards Fair Deep Anomaly Detection—0
Towards Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping—0
Unsupervised Transfer Learning for Anomaly Detection: Application to Complementary Operating Condition Transfer—0
Trustworthiness of X Users: A One-Class Classification Approach—0
Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning—0
Understanding Time Series Anomaly State Detection through One-Class Classification—0
Unsupervised Artifact Detection for Whole Slide Images of Prostate Biopsies—0
Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics—0
Unsupervised Learning of the Set of Local Maxima—0
Use of in-the-wild images for anomaly detection in face anti-spoofing—0
usfAD Based Effective Unknown Attack Detection Focused IDS Framework—0
Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach—0
OCKELM+: Kernel Extreme Learning Machine based One-class Classification using Privileged Information (or KOC+: Kernel Ridge Regression or Least Square SVM with zero bias based One-class Classification using Privileged Information)—0
Learning Off-Road Terrain Traversability with Self-Supervisions Only—0
Learning Polynomial Representations of Physical Objects with Application to Certifying Correct Packing Configurations—0
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