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 2751–2775 of 4856 papers

TitleStatusHype
Temporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection—0
Temporal Convolution-based Hybrid Model Approach with Representation Learning for Real-Time Acoustic Anomaly Detection—0
Temporal cross-validation impacts multivariate time series subsequence anomaly detection evaluation—0
Temporal Divide-and-Conquer Anomaly Actions Localization in Semi-Supervised Videos with Hierarchical Transformer—0
Temporal Graph Networks for Graph Anomaly Detection in Financial Networks—0
Temporal Graphs Anomaly Emergence Detection: Benchmarking For Social Media Interactions—0
Temporal Knowledge Distillation for Time-Sensitive Financial Services Applications—0
Temporal Shift -- Multi-Objective Loss Function for Improved Anomaly Fall Detection—0
TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systems—0
Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review—0
Testing for Typicality with Respect to an Ensemble of Learned Distributions—0
Test Time Training for Industrial Anomaly Segmentation—0
Text-Driven Traffic Anomaly Detection with Temporal High-Frequency Modeling in Driving Videos—0
Text-Guided Variational Image Generation for Industrial Anomaly Detection and Segmentation—0
Textile Anomaly Detection: Evaluation of the State-of-the-Art for Automated Quality Inspection of Carpet—0
Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detection—0
Texture-AD: An Anomaly Detection Dataset and Benchmark for Real Algorithm Development—0
That's BAD: Blind Anomaly Detection by Implicit Local Feature Clustering—0
The 4th AI City Challenge—0
The Clever Hans Effect in Anomaly Detection—0
The Dark Side of Digital Twins: Adversarial Attacks on AI-Driven Water Forecasting—0
The Deep Radial Basis Function Data Descriptor (D-RBFDD) Network: A One-Class Neural Network for Anomaly Detection—0
The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods—0
The Impact of Frequency Bands on Acoustic Anomaly Detection of Machines using Deep Learning Based Model—0
The Inverse Bagging Algorithm: Anomaly Detection by Inverse Bootstrap Aggregating—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