SOTAVerified

Anomaly Detection

Anomaly Detection is a binary classification identifying unusual or unexpected patterns in a dataset, which deviate significantly from the majority of the data. The goal of anomaly detection is to identify such anomalies, which could represent errors, fraud, or other types of unusual events, and flag them for further investigation.

[Image source]: GAN-based Anomaly Detection in Imbalance Problems

Papers

Showing 46264650 of 4856 papers

TitleStatusHype
Catching Anomalous Distributed Photovoltaics: An Edge-based Multi-modal Anomaly Detection0
Practical Machine Learning for Cloud Intrusion Detection: Challenges and the Way Forward0
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges0
To Go or Not To Go? A Near Unsupervised Learning Approach For Robot Navigation0
Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning0
Ignoring Distractors in the Absence of Labels: Optimal Linear Projection to Remove False Positives During Anomaly Detection0
Anomaly Detection in Hierarchical Data Streams under Unknown Models0
Deep and Confident Prediction for Time Series at UberCode1
Medical Image Analysis using Convolutional Neural Networks: A Review0
Abnormal Event Detection in Videos using Generative Adversarial Nets0
Incorporating Feedback into Tree-based Anomaly DetectionCode1
Anomaly Detection: Review and preliminary Entropy method tests0
Anomaly Detection in Wireless Sensor Networks0
Bayesian Learning of Clique Tree Structure0
Explaining Anomalies in Groups with Characterizing Subspace RulesCode0
Brain Abnormality Detection by Deep Convolutional Neural Network0
Deep Learning for Medical Image Analysis0
Energy-based Models for Video Anomaly Detection0
Limiting the Reconstruction Capability of Generative Neural Network using Negative Learning0
Sampling High Throughput Data for Anomaly Detection of Data-Base Activity0
Anomaly Detection with Robust Deep AutoencodersCode0
Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data0
Anomaly Detection on Graph Time Series0
Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS DiagnosisCode0
Anomaly Detection by Robust Statistics0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016Unverified
2CPR-fast(TensorRT)FPS362Unverified
3CPR(TensorRT)FPS130Unverified
4GLASSDetection AUROC99.9Unverified
5UniNetDetection AUROC99.9Unverified
6HETMMDetection AUROC99.8Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9DDADDetection AUROC99.8Unverified
10PBASDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
5DDADDetection AUROC98.9Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
7DiffusionADDetection AUROC98.8Unverified
8GLASSDetection AUROC98.8Unverified
9TransFusionDetection AUROC98.7Unverified
10HETMMDetection AUROC98.1Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3Unverified
2PSADAvg. Detection AUROC94.9Unverified