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 46764700 of 4856 papers

TitleStatusHype
Hybrid Isolation Forest - Application to Intrusion DetectionCode0
Automatic Anomaly Detection in the Cloud Via Statistical LearningCode0
Robust, Deep and Inductive Anomaly DetectionCode0
Anomaly detection and motif discovery in symbolic representations of time series0
Grouped Convolutional Neural Networks for Multivariate Time Series0
Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network0
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker DiscoveryCode0
Statistical Anomaly Detection via Composite Hypothesis Testing for Markov ModelsCode0
Latent Laplacian Maximum Entropy Discrimination for Detection of High-Utility Anomalies0
Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets0
Abnormal Event Detection in Videos using Spatiotemporal AutoencoderCode0
Anomaly Detection Using the Knowledge-based Temporal Abstraction Method0
Known Unknowns: Uncertainty Quality in Bayesian Neural NetworksCode0
The Power of Adaptivity in Identifying Statistical Alternatives0
Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks0
Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models0
The Inverse Bagging Algorithm: Anomaly Detection by Inverse Bootstrap Aggregating0
A Survey of Credit Card Fraud Detection Techniques: Data and Technique Oriented Perspective0
Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methods0
Low Latency Anomaly Detection and Bayesian Network Prediction of Anomaly Likelihood0
One Class Splitting Criteria for Random ForestsCode0
Recurrent Neural Radio Anomaly Detection0
Anomaly Detection with the Voronoi Diagram Evolutionary Algorithm0
A Bayesian Ensemble for Unsupervised Anomaly Detection0
Maximally Divergent Intervals for Anomaly Detection0
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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