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 4576–4600 of 4856 papers

TitleStatusHype
Filtering Approaches for Dealing with Noise in Anomaly Detection—0
Filtering DDoS Attacks from Unlabeled Network Traffic Data Using Online Deep Learning—0
Finding Needle in a Million Metrics: Anomaly Detection in a Large-scale Computational Advertising Platform—0
Finding Pegasus: Enhancing Unsupervised Anomaly Detection in High-Dimensional Data using a Manifold-Based Approach—0
Finding Rats in Cats: Detecting Stealthy Attacks using Group Anomaly Detection—0
Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanations—0
Fine-grained Anomaly Detection via Multi-task Self-Supervision—0
Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification—0
Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data—0
Flashback: Memory-Driven Zero-shot, Real-time Video Anomaly Detection—0
Learning Temporal Causal Sequence Relationships from Real-Time Time-Series—0
FlexParser -- the adaptive log file parser for continuous results in a changing world—0
OneFlow: One-class flow for anomaly detection based on a minimal volume region—0
Flow-based generative models as iterative algorithms in probability space—0
Flow-based Self-supervised Density Estimation for Anomalous Sound Detection—0
Flow-based SVDD for anomaly detection—0
Flow Forecast: A deep learning for time series forecasting, classification, and anomaly detection framework built in PyTorch—0
Flurry: a Fast Framework for Reproducible Multi-layered Provenance Graph Representation Learning—0
"Flux+Mutability": A Conditional Generative Approach to One-Class Classification and Anomaly Detection—0
FM-AE: Frequency-masked Multimodal Autoencoder for Zinc Electrolysis Plate Contact Abnormality Detection—0
FMM-Head: Enhancing Autoencoder-based ECG anomaly detection with prior knowledge—0
Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization—0
Fog Intelligence for Network Anomaly Detection—0
Forecast-based Multi-aspect Framework for Multivariate Time-series Anomaly Detection—0
Forensic Data Analytics for Anomaly Detection in Evolving Networks—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