One-class classifier
24 papers with code • 0 benchmarks • 3 datasets
Benchmarks
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COVID-19-CT-CXR
Simulated micro-Doppler Signatures
Most implemented papers
Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection
xuhongzuo/couta
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To tackle these problems, this paper proposes calibrated one-class classification for anomaly detection, realizing contamination-tolerant, anomaly-informed learning of data normality via uncertainty modeling-based calibration and native anomaly-based calibration.
UNTAG: LEARNING GENERIC FEATURES FOR UNSUPERVISED TYPE-AGNOSTIC DEEPFAKE DETECTION
nesrnesr/UNTAG
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ICASSP 2023
This paper introduces a novel framework for unsupervised type-agnostic deepfake detection called UNTAG.
OCGEC: One-class Graph Embedding Classification for DNN Backdoor Detection
jhy549/ocgec
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We then pre-train a generative self-supervised graph autoencoder (GAE) to better learn the features of benign models in order to detect backdoor models without knowing the attack strategy.
Generative Semi-supervised Graph Anomaly Detection
This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal, contrasting to the unsupervised setting in most GAD studies with a fully unlabeled graph.
