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 151–175 of 4856 papers

TitleStatusHype
GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models—0
Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly DetectionCode2
Towards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compression—0
AI-Powered Anomaly Detection with Blockchain for Real-Time Security and Reliability in Autonomous Vehicles—0
ReplayCAD: Generative Diffusion Replay for Continual Anomaly DetectionCode2
Engineering Risk-Aware, Security-by-Design Frameworks for Assurance of Large-Scale Autonomous AI Models—0
Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly DetectionCode0
Research on Anomaly Detection Methods Based on Diffusion Models—0
Joint Detection of Fraud and Concept Drift inOnline Conversations with LLM-Assisted Judgment—0
Detecting Spelling and Grammatical Anomalies in Russian Poetry Texts—0
Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced DatasetsCode0
Explaining Anomalies with Tensor Networks—0
AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active LearningCode0
CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection—0
Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems—0
Lane-Wise Highway Anomaly Detection—0
Uncertainty-Weighted Image-Event Multimodal Fusion for Video Anomaly DetectionCode1
Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language ModelsCode2
An Explainable Anomaly Detection Framework for Monitoring Depression and Anxiety Using Consumer Wearable Devices—0
Spotting the Unexpected (STU): A 3D LiDAR Dataset for Anomaly Segmentation in Autonomous Driving—0
MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detection—0
Context-Aware Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition—0
Runtime Anomaly Detection for Drones: An Integrated Rule-Mining and Unsupervised-Learning Approach—0
FreCT: Frequency-augmented Convolutional Transformer for Robust Time Series Anomaly Detection—0
SpectrumFM: A Foundation Model for Intelligent Spectrum ManagementCode1
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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