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 27512775 of 4856 papers

TitleStatusHype
Dual-stream spatiotemporal networks with feature sharing for monitoring animals in the home cage0
Proximally Sensitive Error for Anomaly Detection and Feature Learning0
Attack-Agnostic Adversarial Detection0
MAD-EN: Microarchitectural Attack Detection through System-wide Energy ConsumptionCode0
Rethinking Graph Neural Networks for Anomaly DetectionCode2
Robust Projection based Anomaly Extraction (RPE) in Univariate Time-Series0
Grid HTM: Hierarchical Temporal Memory for Anomaly Detection in VideosCode0
Benchmarking Unsupervised Anomaly Detection and Localization0
Unfooling Perturbation-Based Post Hoc ExplainersCode0
Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection0
Ensemble2: Anomaly Detection via EVT-Ensemble Framework for Seasonal KPIs in Communication Network0
Fake It Till You Make It: Towards Accurate Near-Distribution Novelty DetectionCode1
FadMan: Federated Anomaly Detection across Multiple Attributed Networks0
PSL is Dead. Long Live PSL0
Raising the Bar in Graph-level Anomaly DetectionCode1
Are Transformers Effective for Time Series Forecasting?Code4
Attention-based residual autoencoder for video anomaly detectionCode1
Towards Symbolic Time Series Representation Improved by Kernel Density Estimators0
MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly DetectionCode5
Faithful Explanations for Deep Graph Models0
Neural Contextual Bandits Based Dynamic Sensor Selection for Low-Power Body-Area Networks0
Naive Few-Shot Learning: Uncovering the fluid intelligence of machines0
Psychotic Relapse Prediction in Schizophrenia Patients using A Mobile Sensing-based Supervised Deep Learning Model0
GraphAD: A Graph Neural Network for Entity-Wise Multivariate Time-Series Anomaly Detection0
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier ImagesCode1
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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