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 3451–3475 of 4856 papers

TitleStatusHype
TIMo -- A Dataset for Indoor Building Monitoring with a Time-of-Flight Camera—0
Anomaly Detection on IT Operation Series via Online Matrix Profile—0
End-To-End Anomaly Detection for Identifying Malicious Cyber Behavior through NLP-Based Log Embeddings—0
Human readable network troubleshooting based on anomaly detection and feature scoring—0
DeepFlow: Abnormal Traffic Flow Detection Using Siamese Networks—0
Anomaly Detection in Medical Imaging -- A Mini Review—0
Weakly-supervised Joint Anomaly Detection and Classification—0
CloudShield: Real-time Anomaly Detection in the CloudCode0
Abnormal Road Surface Detection Using Wheel Sensor Data—0
Federated Variational Learning for Anomaly Detection in Multivariate Time Series—0
Event2Graph: Event-driven Bipartite Graph for Multivariate Time-series Anomaly Detection—0
Random Subspace Mixture Models for Interpretable Anomaly Detection—0
Unconditional Scene Graph Generation—0
Unsupervised Driver Behavior Profiling leveraging Recurrent Neural Networks—0
CPNet: Cross-Parallel Network for Efficient Anomaly DetectionCode0
Flow-based SVDD for anomaly detection—0
Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Video—0
Transfer Learning Gaussian Anomaly Detection by Fine-tuning Representations—0
Scaling New Peaks: A Viewership-centric Approach to Automated Content Curation—0
Anomaly Detection Based on Generalized Gaussian Distribution approach for Ultra-Wideband (UWB) Indoor Positioning System—0
Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach—0
Ensemble neuroevolution based approach for multivariate time series anomaly detection—0
Locally Interpretable One-Class Anomaly Detection for Credit Card Fraud DetectionCode0
Using a Collated Cybersecurity Dataset for Machine Learning and Artificial Intelligence—0
Deep Anomaly Discovery From Unlabeled Videos via Normality Advantage and Self-Paced Refinement—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