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 2526–2550 of 4856 papers

TitleStatusHype
Scrutinizing Shipment Records To Thwart Illegal Timber Trade—0
SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Images—0
Searching for a Hidden Markov Anomaly over Multiple Processes—0
Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine Learning for Anomaly Detection—0
Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networks—0
Secure Hierarchical Federated Learning in Vehicular Networks Using Dynamic Client Selection and Anomaly Detection—0
Securing Fog-to-Things Environment Using Intrusion Detection System Based On Ensemble Learning—0
Securing Your Transactions: Detecting Anomalous Patterns In XML Documents—0
See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers—0
Self-Attentive Classification-Based Anomaly Detection in Unstructured Logs—0
Self-awareness in Intelligent Vehicles: Experience Based Abnormality Detection—0
Self-awareness in intelligent vehicles: Feature based dynamic Bayesian models for abnormality detection—0
Self-Calibrating Anomaly and Change Detection for Autonomous Inspection Robots—0
Self-Discriminative Modeling for Anomalous Graph Detection—0
Self-Organising Maps in Computer Security—0
Self-Supervised and Interpretable Anomaly Detection using Network Transformers—0
Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook—0
Self-Supervised Anomaly Detection in the Wild: Favor Joint Embeddings Methods—0
Self-Supervised Anomaly Detection of Rogue Soil Moisture Sensors—0
Self-supervised Complex Network for Machine Sound Anomaly Detection—0
Self-Supervised Contrastive Graph Clustering Network via Structural Information Fusion—0
Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection—0
Self-Supervised Guided Segmentation Framework for Unsupervised Anomaly Detection—0
Self-Supervised Iterative Refinement for Anomaly Detection in Industrial Quality Control—0
Self-supervised Learning for Anomaly Detection in Computational Workflows—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