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 1651–1675 of 4856 papers

TitleStatusHype
An Anomaly Detection System Based on Generative Classifiers for Controller Area Network—0
Did You Hear That? Introducing AADG: A Framework for Generating Benchmark Data in Audio Anomaly Detection—0
An Anomaly Detection Method for Satellites Using Monte Carlo Dropout—0
Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation—0
Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability—0
DiffFake: Exposing Deepfakes using Differential Anomaly Detection—0
Data Transformer for Anomalous Trajectory Detection—0
Data refinement for fully unsupervised visual inspection using pre-trained networks—0
Anomaly segmentation model for defects detection in electroluminescence images of heterojunction solar cells—0
Data Quality Monitoring through Transfer Learning on Anomaly Detection for the Hadron Calorimeters—0
Data-Efficient Methods for Dialogue Systems—0
Anomaly Rule Detection in Sequence Data—0
An Adaptive Training-less System for Anomaly Detection in Crowd Scenes—0
A Scalable k-Medoids Clustering via Whale Optimization Algorithm—0
Electrical Grid Anomaly Detection via Tensor Decomposition—0
Emotion-Based Crowd Representation for Abnormality Detection—0
Energy-Based Anomaly Detection and Localization—0
Enhancing Anomaly Detection via Generating Diversified and Hard-to-distinguish Synthetic Anomalies—0
Digraphwave: Scalable Extraction of Structural Node Embeddings via Diffusion on Directed Graphs—0
Dimensionality Increment of PMU Data for Anomaly Detection in Low Observability Power Systems—0
Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder—0
Dimensionality reduction techniques to support insider trading detection—0
Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection—0
A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels—0
Data-Efficient and Interpretable Tabular Anomaly 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