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 3926–3950 of 4856 papers

TitleStatusHype
Online learning of windmill time series using Long Short-term Cognitive Networks—0
Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directions—0
Online Multivariate Anomaly Detection and Localization for High-dimensional Settings—0
Debunking Free Fusion Myth: Online Multi-view Anomaly Detection with Disentangled Product-of-Experts Modeling—0
OnlineSTL: Scaling Time Series Decomposition by 100x—0
Online Time Series Anomaly Detection with State Space Gaussian Processes—0
Online Transition-Based Feature Generation for Anomaly Detection in Concurrent Data Streams—0
On multipolar magnetic anomaly detection: multipolar signal subspaces, an analytical orthonormal basis, multipolar truncature and detection performance—0
On Multi-Session Website Fingerprinting over TLS Handshake—0
On Pixel-level Performance Assessment in Anomaly Detection—0
On the Adversarial Robustness of Benjamini Hochberg—0
On the Connection of Generative Models and Discriminative Models for Anomaly Detection—0
On the Detection of Mixture Distributions with applications to the Most Biased Coin Problem—0
On the Ground Validation of Online Diagnosis with Twitter and Medical Records—0
On the Impact of Object and Sub-component Level Segmentation Strategies for Supervised Anomaly Detection within X-ray Security Imagery—0
On the Nature and Types of Anomalies: A Review of Deviations in Data—0
On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks—0
On the Properties of Kullback-Leibler Divergence Between Multivariate Gaussian Distributions—0
On The Relationship between Visual Anomaly-free and Anomalous Representations—0
On the Robustness and Anomaly Detection of Sparse Neural Networks—0
On the Runtime-Efficacy Trade-off of Anomaly Detection Techniques for Real-Time Streaming Data—0
On the Usage of Generative Models for Network Anomaly Detection in Multivariate Time-Series—0
On unsupervised-supervised risk and one-class neural networks—0
Open-Set Graph Anomaly Detection via Normal Structure Regularisation—0
Open-Set Multivariate Time-Series Anomaly Detection—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