Supervised Anomaly Detection
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Benchmarks
Most implemented
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
Deep Semi-Supervised Anomaly Detection
Deep Weakly-supervised Anomaly Detection
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization
Supervised Anomaly Detection for Complex Industrial Images
Papers
ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection
The deployment of Industrial Anomaly Detection (IAD) in real-world manufacturing frequently encounters a challenging cold-start bottleneck, in which limited normal samples fail to represent the full normal distribution a…
Supervised Anomaly DetectionAn AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response
University Academic Management Information Systems (ACMIS) are high-value targets for a wide spectrum of security threats including brute-force login attacks, payment fraud, privilege escalation, insider data theft, and …
Supervised Anomaly DetectionIntrusion DetectionRethinking Weak Supervision in Anomaly Detection: A Comprehensive Benchmark
Weakly supervised anomaly detection (WSAD) has developed in three primary directions: incomplete, inexact, and inaccurate supervision. However, these directions remain isolated, lacking a unified framework to assess whet…
Supervised Anomaly DetectionGeneral ClassificationMixture Prototype Flow Matching for Open-Set Supervised Anomaly Detection
Open-set supervised anomaly detection (OSAD) aims to identify unseen anomalies using limited anomalous supervision. However, existing prototype-based methods typically model normal data via a unimodal Gaussian prior, fai…
Supervised Anomaly DetectionKidney Cancer Detection Using 3D-Based Latent Diffusion Models
In this work, we present a novel latent diffusion-based pipeline for 3D kidney anomaly detection on contrast-enhanced abdominal CT. The method combines Denoising Diffusion Probabilistic Models (DDPMs), Denoising Diffusio…
Supervised Anomaly DetectionCEDL: Centre-Enhanced Discriminative Learning for Anomaly Detection
Supervised anomaly detection methods perform well in identifying known anomalies that are well represented in the training set. However, they often struggle to generalise beyond the training distribution due to decision …
Supervised Anomaly Detection