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 426450 of 4856 papers

TitleStatusHype
Fully Convolutional Cross-Scale-Flows for Image-based Defect DetectionCode1
Future Frame Prediction for Anomaly Detection -- A New BaselineCode1
Future Frame Prediction for Anomaly Detection – A New BaselineCode1
GAD-NR: Graph Anomaly Detection via Neighborhood ReconstructionCode1
GAN Ensemble for Anomaly DetectionCode1
Challenges in Visual Anomaly Detection for Mobile RobotsCode1
Generalized Out-of-Distribution Detection: A SurveyCode1
Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth SimulationCode1
Exathlon: A Benchmark for Explainable Anomaly Detection over Time SeriesCode1
CODiT: Conformal Out-of-Distribution Detection in Time-Series DataCode1
Catching Both Gray and Black Swans: Open-set Supervised Anomaly DetectionCode1
Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly TypesCode1
Generator Versus Segmentor: Pseudo-healthy SynthesisCode1
Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly DetectionCode1
GlocalCLIP: Object-agnostic Global-Local Prompt Learning for Zero-shot Anomaly DetectionCode1
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly DetectionCode1
Graph Anomaly Detection with Unsupervised GNNsCode1
Can Multimodal LLMs Perform Time Series Anomaly Detection?Code1
Graph-level Anomaly Detection via Hierarchical Memory NetworksCode1
CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event SequencesCode1
Graph Neural Networks based Log Anomaly Detection and ExplanationCode1
CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and ForecastingCode1
Calibrated One-class Classification for Unsupervised Time Series Anomaly DetectionCode1
HealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human DiseaseCode1
Camouflaged Object 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