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 226250 of 4856 papers

TitleStatusHype
Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth SimulationCode1
CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly DetectionCode1
DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly DetectionCode1
CHAD: Charlotte Anomaly DatasetCode1
DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security ApplicationsCode1
AlerTiger: Deep Learning for AI Model Health Monitoring at LinkedInCode1
Deep Anomaly Detection with Outlier ExposureCode1
Deep Contrastive One-Class Time Series Anomaly DetectionCode1
Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection in Machine Condition SoundsCode1
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing FlowsCode1
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly DetectionCode1
Challenges in Visual Anomaly Detection for Mobile RobotsCode1
Deep Graph-level Anomaly Detection by Glocal Knowledge DistillationCode1
CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and ForecastingCode1
Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly DetectionCode1
Deep Learning in Latent Space for Video Prediction and CompressionCode1
Deep Reinforcement Learning for Cost-Effective Medical DiagnosisCode1
ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly DetectionCode1
DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 PandemicCode1
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly LocalizationCode1
Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRICode1
Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video EventsCode1
Demystifying and Extracting Fault-indicating Information from Logs for Failure DiagnosisCode1
DenseHybrid: Hybrid Anomaly Detection for Dense Open-set RecognitionCode1
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly 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