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 3776–3800 of 4856 papers

TitleStatusHype
Towards the Development of Entropy-Based Anomaly Detection in an Astrophysics Simulation—0
Simulation-Assisted Decorrelation for Resonant Anomaly DetectionCode0
Multivariate Time-series Anomaly Detection via Graph Attention NetworkCode1
Data Anomaly Detection for Structural Health Monitoring of Bridges using Shapelet Transform—0
Connecting Web Event Forecasting with Anomaly Detection: A Case Study on Enterprise Web Applications Using Self-Supervised Neural Networks—0
Anomaly Detection by Recombining Gated Unsupervised ExpertsCode0
Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly DetectionCode0
Puzzle-AE: Novelty Detection in Images through Solving PuzzlesCode0
Improved anomaly detection by training an autoencoder with skip connections on images corrupted with Stain-shaped noiseCode1
GAN-based Anomaly Detection in Imbalance Problems—0
Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing FlowsCode1
The Impact of Discretization Method on the Detection of Six Types of Anomalies in DatasetsCode0
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in VideoCode1
A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels—0
Training robust anomaly detection using ML-Enhanced simulations—0
Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video EventsCode1
Defending Water Treatment Networks: Exploiting Spatio-temporal Effects for Cyber Attack Detection—0
DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 PandemicCode1
Applying Surface Normal Information in Drivable Area and Road Anomaly Detection for Ground Mobile RobotsCode1
Hybrid Deep Neural Networks to Infer State Models of Black-Box SystemsCode0
Automated Model Selection for Time-Series Anomaly Detection—0
USAD: UnSupervised Anomaly Detection on Multivariate Time SeriesCode1
ADIC: Anomaly Detection Integrated Circuit in 65nm CMOS utilizing Approximate Computing—0
Self-Attentive Classification-Based Anomaly Detection in Unstructured Logs—0
TAnoGAN: Time Series Anomaly Detection with Generative Adversarial NetworksCode1
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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