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 3476–3500 of 4856 papers

TitleStatusHype
Anomaly Detection Based on Indicators Aggregation—0
Anomaly Detection Based on Isolation Mechanisms: A Survey—0
Anomaly Detection Based on Multiple-Hypothesis Autoencoder—0
Anomaly Detection Based on Selection and Weighting in Latent Space—0
Anomaly Detection Based on Unsupervised Disentangled Representation Learning in Combination with Manifold Learning—0
Anomaly Detection based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation—0
Anomaly Detection by Adapting a pre-trained Vision Language Model—0
Anomaly Detection By Autoencoder Based On Weighted Frequency Domain Loss—0
Anomaly Detection by Context Contrasting—0
Anomaly Detection by One Class Latent Regularized Networks—0
Anomaly Detection by Robust Statistics—0
Anomaly Detection Dataset for Industrial Control Systems—0
Anomaly Detection for Aggregated Data Using Multi-Graph Autoencoder—0
Anomaly Detection for an E-commerce Pricing System—0
An Improved Anomaly Detection Model for Automated Inspection of Power Line Insulators—0
Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning—0
Anomaly Detection for Fraud in Cryptocurrency Time Series—0
Anomaly Detection for High-Dimensional Data Using Large Deviations Principle—0
Anomaly Detection for imbalanced datasets with Deep Generative Models—0
Anomaly Detection for Incident Response at Scale—0
Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review—0
Anomaly Detection for Industrial Big Data—0
Anomaly Detection for Multivariate Time Series of Exotic Supernovae—0
Anomaly Detection for Multivariate Time Series on Large-scale Fluid Handling Plant Using Two-stage Autoencoder—0
Anomaly Detection for Network Connection Logs—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