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 501525 of 4856 papers

TitleStatusHype
Incomplete Multimodal Industrial Anomaly Detection via Cross-Modal DistillationCode1
Anomaly Detection for Solder Joints Using β-VAECode1
Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRICode1
Anomaly Detection for Time Series Using VAE-LSTM Hybrid ModelCode1
MCDDPM: Multichannel Conditional Denoising Diffusion Model for Unsupervised Anomaly Detection in Brain MRICode1
MDF-Net for abnormality detection by fusing X-rays with clinical dataCode1
Challenges in Visual Anomaly Detection for Mobile RobotsCode1
Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour modelsCode1
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing FlowsCode1
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly LocalizationCode1
CHAD: Charlotte Anomaly DatasetCode1
ChatGPT for Digital Forensic Investigation: The Good, The Bad, and The UnknownCode1
Catching Both Gray and Black Swans: Open-set Supervised Anomaly DetectionCode1
Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly DetectionCode1
Anomaly Detection in Emails using Machine Learning and Header InformationCode1
Alleviating Structural Distribution Shift in Graph Anomaly DetectionCode1
CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event SequencesCode1
CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and ForecastingCode1
Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth SimulationCode1
Camouflaged Object DetectionCode1
Calibrated One-class Classification for Unsupervised Time Series Anomaly DetectionCode1
Can LLMs Understand Time Series Anomalies?Code1
CableInspect-AD: An Expert-Annotated Anomaly Detection DatasetCode1
A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data StreamsCode1
CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality DetectionCode1
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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