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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3851–3875 of 4856 papers

TitleStatusHype
A Vision Inspired Neural Network for Unsupervised Anomaly Detection in Unordered Data—0
Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks—0
Background subtraction on depth videos with convolutional neural networks—0
Back Home: A Machine Learning Approach to Seashell Classification and Ecosystem Restoration—0
Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies with an Integrated Statistical and Neural Framework—0
A Deep Learning Approach to Anomaly Sequence Detection for High-Resolution Monitoring of Power Systems—0
BadSAD: Clean-Label Backdoor Attacks against Deep Semi-Supervised Anomaly Detection—0
Bagged Regularized k-Distances for Anomaly Detection—0
Balancing Privacy and Action Performance: A Penalty-Driven Approach to Image Anonymization—0
Ball Mill Fault Prediction Based on Deep Convolutional Auto-Encoding Network—0
Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Sounds—0
Battery Cloud with Advanced Algorithms—0
Battery State of Health Estimation Using LLM Framework—0
Bayesian Anomaly Detection and Classification—0
Bayesian Anomaly Detection Using Extreme Value Theory—0
Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection—0
Bayesian generative models can flag performance loss, bias, and out-of-distribution image content—0
Bayesian Hypernetworks—0
Bayesian Learning of Clique Tree Structure—0
Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection—0
Bayesian Time Series Forecasting with Change Point and Anomaly Detection—0
Behavioral Anomaly Detection in Distributed Systems via Federated Contrastive Learning—0
Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics—0
Benchmarking Unsupervised Anomaly Detection and Localization—0
Beta quantile regression for robust estimation of uncertainty in the presence of outliers—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