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 2626–2650 of 4856 papers

TitleStatusHype
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
Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization—0
Beyond Forecasting: Compositional Time Series Reasoning for End-to-End Task Execution—0
Beyond Window-Based Detection: A Graph-Centric Framework for Discrete Log Anomaly Detection—0
Bias in Unsupervised Anomaly Detection in Brain MRI—0
Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity—0
Bidirectional skip-frame prediction for video anomaly detection with intra-domain disparity-driven attention—0
Big data analysis and distributed deep learning for next-generation intrusion detection system optimization—0
Bioinspired Cortex-based Fast Codebook Generation—0
Blockchain-enabled fraud discovery through abnormal smart contract detection on Ethereum—0
Blockchain Large Language Models—0
Blockchain Meets Adaptive Honeypots: A Trust-Aware Approach to Next-Gen IoT Security—0
BlockFound: Customized blockchain foundation model for anomaly 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
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