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 1351–1375 of 4856 papers

TitleStatusHype
Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable Model—0
ComplexVAD: Detecting Interaction Anomalies in Video—0
Sequential PatchCore: Anomaly Detection for Surface Inspection using Synthetic Impurities—0
Transformer-based Multivariate Time Series Anomaly Localization—0
Multiple-Input Variational Auto-Encoder for Anomaly Detection in Heterogeneous Data—0
Detecting Contextual Anomalies by Discovering Consistent Spatial Regions—0
STTS-EAD: Improving Spatio-Temporal Learning Based Time Series Prediction via—0
Unsupervised Feature Construction for Anomaly Detection in Time Series -- An Evaluation—0
ADKGD: Anomaly Detection in Knowledge Graphs with Dual-Channel TrainingCode0
Driver Age and Its Effect on Key Driving Metrics: Insights from Dynamic Vehicle Data—0
Generative AI Enabled Robust Sensor Placement in Cyber-Physical Power Systems: A Graph Diffusion Approach—0
Active Rule Mining for Multivariate Anomaly Detection in Radio Access Networks—0
Explaining Deep Learning-based Anomaly Detection in Energy Consumption Data by Focusing on Contextually Relevant Data—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
EVA-S2PLoR: A Secure Element-wise Multiplication Meets Logistic Regression on Heterogeneous Database—0
Learning Compact and Robust Representations for Anomaly Detection—0
Back Home: A Machine Learning Approach to Seashell Classification and Ecosystem Restoration—0
Quantum Hybrid Support Vector Machines for Stress Detection in Older Adults—0
Planing It by Ear: Convolutional Neural Networks for Acoustic Anomaly Detection in Industrial Wood PlanersCode0
Leveraging Registers in Vision Transformers for Robust Adaptation—0
KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration—0
Unsupervised Tomato Split Anomaly Detection using Hyperspectral Imaging and Variational Autoencoders—0
On the Adversarial Robustness of Benjamini Hochberg—0
GraphDART: Graph Distillation for Efficient Advanced Persistent Threat 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
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