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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3801–3825 of 4856 papers

TitleStatusHype
Multimodal video analysis for crowd anomaly detection using open access tourism cameras—0
Multi-Perspective Anomaly Detection—0
Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection—0
Multiple Abnormality Detection for Automatic Medical Image Diagnosis Using Bifurcated Convolutional Neural Network—0
Multiple Inputs Neural Networks for Medicare fraud Detection—0
Multiple-Input Variational Auto-Encoder for Anomaly Detection in Heterogeneous Data—0
Multiple Instance Learning for Detecting Anomalies over Sequential Real-World Datasets—0
Multiple profiles sensor-based monitoring and anomaly detection—0
MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast—0
Multiresolution Feature Guidance Based Transformer for Anomaly Detection—0
Multi-scale Anomaly Detection for Big Time Series of Industrial Sensors—0
Multi-Scale Convolutional LSTM with Transfer Learning for Anomaly Detection in Cellular Networks—0
Multiscale Fusion for Abnormality Detection and Localization of Distributed Parameter Systems—0
Multi-scale Masked Autoencoder for Electrocardiogram Anomaly Detection—0
Multi-Scale Memory Comparison for Zero-/Few-Shot Anomaly Detection—0
Multi-Scale Patch-Based Representation Learning for Image Anomaly Detection and Segmentation—0
Multi-scale Spatial-temporal Interaction Network for Video Anomaly Detection—0
Multi-Scale Video Anomaly Detection by Multi-Grained Spatio-Temporal Representation Learning—0
Multi-Source Anomaly Detection in Distributed IT Systems—0
Multi-Stage Fault Warning for Large Electric Grids Using Anomaly Detection and Machine Learning—0
Multi-Stage Prompt Inference Attacks on Enterprise LLM Systems—0
Multi-task Feature Selection based Anomaly Detection—0
Multi-Task Learning based Video Anomaly Detection with Attention—0
Multi-Task Self-Supervised Time-Series Representation Learning—0
Shifting Transformation Learning for Out-of-Distribution Detection—0
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All datasetsMVTec ADVisAMVTec LOCO ADOne-class CIFAR-10CUHK AvenueShanghaiTechFishyscapes L&FUCR Anomaly ArchiveMPDDBTADOne-class CIFAR-100UBnormal

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016—Unverified
2CPR-fast(TensorRT)FPS362—Unverified
3CPR(TensorRT)FPS130—Unverified
4GLASSDetection AUROC99.9—Unverified
5UniNetDetection AUROC99.9—Unverified
6HETMMDetection AUROC99.8—Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9DDADDetection AUROC99.8—Unverified
10PBASDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9—Unverified
5DDADDetection AUROC98.9—Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
7DiffusionADDetection AUROC98.8—Unverified
8GLASSDetection AUROC98.8—Unverified
9TransFusionDetection AUROC98.7—Unverified
10HETMMDetection AUROC98.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3—Unverified
2PSADAvg. Detection AUROC94.9—Unverified