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 19762000 of 4856 papers

TitleStatusHype
Applied Bayesian Structural Health Monitoring: inclinometer data anomaly detection and forecasting0
A Comparative Study of Machine Learning Algorithms for Anomaly Detection in Industrial Environments: Performance and Environmental Impact0
Interpretable Anomaly Detection in Cellular Networks by Learning Concepts in Variational Autoencoders0
Exploring Dual Model Knowledge Distillation for Anomaly Detection0
Learning normal asymmetry representations for homologous brain structuresCode0
Anomaly Detection in Networks via Score-Based Generative ModelsCode0
Precursor-of-Anomaly Detection for Irregular Time SeriesCode1
Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly DetectionCode1
Anomaly Detection with Score Distribution DiscriminationCode1
Autoencoders for Real-Time SUEP Detection0
OptIForest: Optimal Isolation Forest for Anomaly DetectionCode0
Targeted collapse regularized autoencoder for anomaly detection: black hole at the center0
Triggering Dark Showers with Conditional Dual Auto-EncodersCode0
GADBench: Revisiting and Benchmarking Supervised Graph Anomaly DetectionCode1
End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection0
Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly DetectorsCode1
Chili Pepper Disease Diagnosis via Image Reconstruction Using GrabCut and Generative Adversarial Serial Autoencoder0
BMAD: Benchmarks for Medical Anomaly DetectionCode1
Machine Learning for Real-Time Anomaly Detection in Optical Networks0
Non-contact Sensing for Anomaly Detection in Wind Turbine Blades: A focus-SVDD with Complex-Valued Auto-Encoder Approach0
Pattern Mining for Anomaly Detection in Graphs: Application to Fraud in Public ProcurementCode1
Improving Generalizability of Graph Anomaly Detection Models via Data AugmentationCode1
Towards exploring adversarial learning for anomaly detection in complex driving scenes0
Multi-scale Spatial-temporal Interaction Network for Video Anomaly Detection0
Tailoring Machine Learning for Process Mining0
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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