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 576600 of 4856 papers

TitleStatusHype
Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly DetectionCode1
Anomaly detection in surveillance videos using transformer based attention modelCode1
Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive AlignmentCode1
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly LocalizationCode1
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted InstancesCode1
Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and ReasoningCode1
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data StreamsCode1
DAGAD: Data Augmentation for Graph Anomaly DetectionCode1
DATE: Detecting Anomalies in Text via Self-Supervision of TransformersCode1
Building an Automated and Self-Aware Anomaly Detection SystemCode1
Deep Anomaly Detection on Attributed NetworksCode1
Deep Anomaly Detection Using Geometric TransformationsCode1
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly DetectionCode1
Anomaly Detection in Video Sequences: A Benchmark and Computational ModelCode1
Alleviating Structural Distribution Shift in Graph Anomaly DetectionCode1
Deep Generative Classification of Blood Cell MorphologyCode1
Anomaly Detection in IR Images of PV Modules using Supervised Contrastive LearningCode1
Anomaly Detection in Video via Self-Supervised and Multi-Task LearningCode1
Deep Isolation Forest for Anomaly DetectionCode1
Deep Learning for Anomaly Detection in Log Data: A SurveyCode1
Deep Learning for Time Series Anomaly Detection: A SurveyCode1
C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic ForecastingCode1
Anomaly Detection under Distribution ShiftCode1
Broiler-Net: A Deep Convolutional Framework for Broiler Behavior Analysis in Poultry HousesCode1
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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