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 1701–1725 of 4856 papers

TitleStatusHype
Distributed-MPC with Data-Driven Estimation of Bus Admittance Matrix in Voltage Control—0
Distributed optimization in wireless sensor networks: an island-model framework—0
Data-Driven Construction of Data Center Graph of Things for Anomaly Detection—0
A Stacked Autoencoder Neural Network based Automated Feature Extraction Method for Anomaly detection in On-line Condition Monitoring—0
Data-Driven Thermal Anomaly Detection in Large Battery Packs—0
ASTD Patterns for Integrated Continuous Anomaly Detection In Data Logs—0
ACE -- An Anomaly Contribution Explainer for Cyber-Security Applications—0
Diverse Counterfactual Explanations for Anomaly Detection in Time Series—0
A Comparison of Deep Learning Architectures for Spacecraft Anomaly Detection—0
Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection—0
Data Drift Monitoring for Log Anomaly Detection Pipelines—0
Data Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection—0
Dividing Deep Learning Model for Continuous Anomaly Detection of Inconsistent ICT Systems—0
Anomalous Sound Detection using Audio Representation with Machine ID based Contrastive Learning Pretraining—0
A Subspace Projection Approach to Autoencoder-based Anomaly Detection—0
Do autoencoders need a bottleneck for anomaly detection?—0
DOC3-Deep One Class Classification using Contradictions—0
DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection—0
Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?—0
Analyzing Business Process Anomalies Using Autoencoders—0
Does Your Phone Know Your Touch?—0
Data Augmentation by AutoEncoders for Unsupervised Anomaly Detection—0
Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection—0
Domain Adaptation via Anaomaly Detection—0
Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspection—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