SOTAVerified

Semi-supervised Anomaly Detection

Papers

Showing 125 of 76 papers

TitleStatusHype
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level LatenciesCode2
Spatial-aware Attention Generative Adversarial Network for Semi-supervised Anomaly Detection in Medical ImageCode1
NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly GenerationCode1
ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced RecognitionCode1
RoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous SupervisionCode1
On Diffusion Modeling for Anomaly DetectionCode1
SAD: Semi-Supervised Anomaly Detection on Dynamic GraphsCode1
Iterative weak/self-supervised classification framework for abnormal events detectionCode1
Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour modelsCode1
A^3: Activation Anomaly AnalysisCode1
Semi-supervised Anomaly Detection using AutoEncodersCode1
Real-world Anomaly Detection in Surveillance VideosCode1
Bridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies0
Enhanced semi-supervised stamping process monitoring with physically-informed feature extraction0
Automated Processing of eXplainable Artificial Intelligence Outputs in Deep Learning Models for Fault Diagnostics of Large Infrastructures0
Semi-supervised Anomaly Detection with Extremely Limited Labels in Dynamic Graphs0
BadSAD: Clean-Label Backdoor Attacks against Deep Semi-Supervised Anomaly Detection0
Deep evolving semi-supervised anomaly detection0
PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time SeriesCode0
SADDE: Semi-supervised Anomaly Detection with Dependable ExplanationsCode0
Directional anomaly detection0
Efficient Quantum One-Class Support Vector Machines for Anomaly Detection Using Randomized Measurements and Variable Subsampling0
Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty DetectionCode0
Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled DataCode0
Semi-supervised Anomaly Detection via Adaptive Reinforcement Learning-Enabled Method with Causal Inference for Sensor SignalsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SS-Model + WS-Model + Sultani et al.AUC0.85Unverified
2SS-ModelAUC0.82Unverified
3LSTM-AEAUC0.54Unverified
4s2-VAEAUC0.54Unverified