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

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Showing 3501–3525 of 4856 papers

TitleStatusHype
PICASO: Permutation-Invariant Cascaded Attentional Set OperatorCode0
Time Series Anomaly Detection for Smart Grids: A Survey—0
Contrastive Predictive Coding for Anomaly Detection—0
Detection of Abnormal Behavior with Self-Supervised Gaze Estimation—0
Deep learning approaches to Earth Observation change detection—0
LATTE: LSTM Self-Attention based Anomaly Detection in Embedded Automotive Platforms—0
Attack Rules: An Adversarial Approach to Generate Attacks for Industrial Control Systems using Machine Learning—0
Anomaly Detection in Residential Video Surveillance on Edge Devices in IoT Framework—0
Explainable AI (XAI) for PHM of Industrial Asset: A State-of-The-Art, PRISMA-Compliant Systematic Review—0
Anomaly Detection Based on Multiple-Hypothesis Autoencoder—0
On Generalization of Graph Autoencoders with Adversarial TrainingCode0
New Methods and Datasets for Group Anomaly Detection From Fundamental Physics—0
A Unified Off-Policy Evaluation Approach for General Value Function—0
Anomaly Detection using Edge Computing in Video Surveillance System: Review—0
T-LoHo: A Bayesian Regularization Model for Structured Sparsity and Smoothness on GraphsCode0
Detecting Outliers with Poisson Image InterpolationCode0
Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving—0
A Typology of Data Anomalies—0
Clustering of Time Series Data with Prior Geographical Information—0
Online learning of windmill time series using Long Short-term Cognitive Networks—0
One-class Steel Detector Using Patch GAN Discriminator for Visualising Anomalous Feature Map—0
Anomaly Detection: How to Artificially Increase your F1-Score with a Biased Evaluation ProtocolCode0
Unsupervised Detection of Anomalous Sound for Machine Condition Monitoring using Fully Connected U-Net—0
A Method for Detecting Abnormal Data of Network Nodes Based on Convolutional Neural Network—0
Approximate Maximum Halfspace Discrepancy—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