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 501–525 of 4856 papers

TitleStatusHype
Transformer-based Multivariate Time Series Anomaly Localization—0
Detecting Contextual Anomalies by Discovering Consistent Spatial Regions—0
Multiple-Input Variational Auto-Encoder for Anomaly Detection in Heterogeneous Data—0
Unsupervised Feature Construction for Anomaly Detection in Time Series -- An Evaluation—0
STTS-EAD: Improving Spatio-Temporal Learning Based Time Series Prediction via—0
ADKGD: Anomaly Detection in Knowledge Graphs with Dual-Channel TrainingCode0
Generative AI Enabled Robust Sensor Placement in Cyber-Physical Power Systems: A Graph Diffusion Approach—0
Driver Age and Its Effect on Key Driving Metrics: Insights from Dynamic Vehicle Data—0
Exploring Pose-Based Anomaly Detection for Retail Security: A Real-World Shoplifting Dataset and BenchmarkCode1
Active Rule Mining for Multivariate Anomaly Detection in Radio Access Networks—0
Synergizing Large Language Models and Task-specific Models for Time Series Anomaly Detection—0
Evidential Deep Learning for Uncertainty Quantification and Out-of-Distribution Detection in Jet Identification using Deep Neural NetworksCode0
Explaining Deep Learning-based Anomaly Detection in Energy Consumption Data by Focusing on Contextually Relevant Data—0
Learning Compact and Robust Representations for Anomaly Detection—0
EVA-S2PLoR: A Secure Element-wise Multiplication Meets Logistic Regression on Heterogeneous Database—0
Quantum Hybrid Support Vector Machines for Stress Detection in Older Adults—0
Leveraging Registers in Vision Transformers for Robust Adaptation—0
Planing It by Ear: Convolutional Neural Networks for Acoustic Anomaly Detection in Industrial Wood PlanersCode0
Back Home: A Machine Learning Approach to Seashell Classification and Ecosystem Restoration—0
KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration—0
On the Adversarial Robustness of Benjamini Hochberg—0
GraphDART: Graph Distillation for Efficient Advanced Persistent Threat Detection—0
Unsupervised Tomato Split Anomaly Detection using Hyperspectral Imaging and Variational Autoencoders—0
Self-Supervised Learning for Detecting AI-Generated Faces as AnomaliesCode0
HSTforU: anomaly detection in aerial and ground-based videos with hierarchical spatio-temporal transformer for U-netCode1
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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