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 176–200 of 4856 papers

TitleStatusHype
MARS: Defending Unmanned Aerial Vehicles From Attacks on Inertial Sensors with Model-based Anomaly Detection and Recovery—0
VideoHallu: Evaluating and Mitigating Multi-modal Hallucinations on Synthetic Video UnderstandingCode1
CostFilter-AD: Enhancing Anomaly Detection through Matching Cost FilteringCode2
Explainable Machine Learning for Cyberattack Identification from Traffic FlowsCode0
Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networks—0
Quantum Support Vector Regression for Robust Anomaly Detection—0
AI-Driven IRM: Transforming insider risk management with adaptive scoring and LLM-based threat detection—0
CICADA: Cross-Domain Interpretable Coding for Anomaly Detection and Adaptation in Multivariate Time Series—0
LLM-Based Threat Detection and Prevention Framework for IoT Ecosystems—0
Enhanced semi-supervised stamping process monitoring with physically-informed feature extraction—0
Convolutional Autoencoders for Data Compression and Anomaly Detection in Small Satellite Technologies—0
On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks—0
GiBy: A Giant-Step Baby-Step Classifier For Anomaly Detection In Industrial Control Systems—0
LR-IAD:Mask-Free Industrial Anomaly Detection with Logical ReasoningCode0
The Dark Side of Digital Twins: Adversarial Attacks on AI-Driven Water Forecasting—0
AGATE: Stealthy Black-box Watermarking for Multimodal Model Copyright Protection—0
Reservoir-enhanced Segment Anything Model for Subsurface DiagnosisCode5
Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics—0
Performance of Machine Learning Classifiers for Anomaly Detection in Cyber Security ApplicationsCode0
Time and Frequency Domain-based Anomaly Detection in Smart Meter Data for Distribution Network Studies—0
Statistical Inference for Clustering-based Anomaly Detection—0
Fault Diagnosis in New Wind Turbines using Knowledge from Existing Turbines by Generative Domain Adaptation—0
Quantum Autoencoder for Multivariate Time Series Anomaly Detection—0
Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications—0
Research on Cloud Platform Network Traffic Monitoring and Anomaly Detection System based on Large Language Models—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