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 2851–2875 of 4856 papers

TitleStatusHype
syslrn: Learning What to Monitor for Efficient Anomaly DetectionCode0
AnoDFDNet: A Deep Feature Difference Network for Anomaly DetectionCode1
Radial Autoencoders for Enhanced Anomaly Detection—0
Contextual Information Based Anomaly Detection for a Multi-Scene UAV Aerial VideosCode0
FlexFringe: Modeling Software Behavior by Learning Probabilistic AutomataCode1
Catching Both Gray and Black Swans: Open-set Supervised Anomaly DetectionCode1
Semi-supervised anomaly detection algorithm based on KL divergence (SAD-KL)—0
PAEDID: Patch Autoencoder Based Deep Image Decomposition For Pixel-level Defective Region Segmentation—0
A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection—0
Learning to Adapt to Unseen Abnormal Activities under Weak SupervisionCode1
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos—0
Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies—0
From MIM-Based GAN to Anomaly Detection:Event Probability Influence on Generative Adversarial Networks—0
Bayesian Nonparametric Submodular Video Partition for Robust Anomaly DetectionCode0
SIFT and SURF based feature extraction for the anomaly detectionCode0
Domain-Generalized Textured Surface Anomaly Detection—0
Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame DetectionCode1
Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked AutoencoderCode0
Two methods for Jamming Identification in UAVs Networks using New Synthetic Dataset—0
FGAN: Federated Generative Adversarial Networks for Anomaly Detection in Network Traffic—0
ASE: Anomaly Scoring Based Ensemble Learning for Imbalanced Datasets—0
Diverse Counterfactual Explanations for Anomaly Detection in Time Series—0
AnoViT: Unsupervised Anomaly Detection and Localization with Vision Transformer-based Encoder-DecoderCode1
Subspace Modeling for Fast Out-Of-Distribution and Anomaly Detection—0
Learning from Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image AnalysisCode0
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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