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 2726–2750 of 4856 papers

TitleStatusHype
An Asymmetric Loss with Anomaly Detection LSTM Framework for Power Consumption Prediction—0
Conformalized Semi-supervised Random Forest for Classification and Abnormality DetectionCode0
Unsupervised Ensemble Methods for Anomaly Detection in PLC-based Process Control—0
Multivariate Time Series Anomaly Detection via Dynamic Graph Forecasting—0
Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial NetworksCode0
Leveraging a Probabilistic PCA Model to Understand the Multivariate Statistical Network Monitoring Framework for Network Security Anomaly Detection—0
An optimization method for out-of-distribution anomaly detection models—0
Eloss in the way: A Sensitive Input Quality Metrics for Intelligent DrivingCode0
Real-Time Outlier Detection with Dynamic Process Limits—0
Graph Anomaly Detection in Time Series: A Survey—0
BSSAD: Towards A Novel Bayesian State-Space Approach for Anomaly Detection in Multivariate Time Series—0
Approximating DTW with a convolutional neural network on EEG data—0
FractalAD: A simple industrial anomaly detection method using fractal anomaly generation and backbone knowledge distillationCode0
Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore—0
Parkinson gait modelling from an anomaly deep representation—0
Coincident Learning for Unsupervised Anomaly Detection—0
Incentive-weighted Anomaly Detection for False Data Injection Attacks Against Smart Meter Load Profiles—0
PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning—0
Unravelling physics beyond the standard model with classical and quantum anomaly detection—0
A convolutional neural network of low complexity for tumor anomaly detection—0
DeepADMR: A Deep Learning based Anomaly Detection for MANET Routing—0
Adapting the Hypersphere Loss Function from Anomaly Detection to Anomaly Segmentation—0
Contracting Skeletal Kinematics for Human-Related Video Anomaly DetectionCode0
A Tale of Two Latent Flows: Learning Latent Space Normalizing Flow with Short-run Langevin Flow for Approximate Inference—0
Condition monitoring and anomaly detection in cyber-physical systems—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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (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