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 3226–3250 of 4856 papers

TitleStatusHype
DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectionCode1
A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame PredictionCode1
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
Anomaly Detection Based on Generalized Gaussian Distribution approach for Ultra-Wideband (UWB) Indoor Positioning System—0
Transfer Learning Gaussian Anomaly Detection by Fine-tuning Representations—0
Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Video—0
Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach—0
Scaling New Peaks: A Viewership-centric Approach to Automated Content Curation—0
P-WAE: Generalized Patch-Wasserstein Autoencoder for Anomaly ScreeningCode1
Ensemble neuroevolution based approach for multivariate time series anomaly detection—0
Unsupervised Image Anomaly Detection and Segmentation Based on Pre-trained Feature MappingCode1
Locally Interpretable One-Class Anomaly Detection for Credit Card Fraud DetectionCode0
Using a Collated Cybersecurity Dataset for Machine Learning and Artificial Intelligence—0
Log-based Anomaly Detection Without Log ParsingCode1
Deep Anomaly Discovery From Unlabeled Videos via Normality Advantage and Self-Paced Refinement—0
Unsupervised Detection of Lung Nodules in Chest Radiography Using Generative Adversarial Networks—0
HTTP2vec: Embedding of HTTP Requests for Detection of Anomalous Traffic—0
Efficacy of Statistical and Artificial Intelligence-based False Information Cyberattack Detection Models for Connected Vehicles—0
Explainable Deep Few-shot Anomaly Detection with Deviation NetworksCode1
HR-Crime: Human-Related Anomaly Detection in Surveillance Videos—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