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 2301–2325 of 4856 papers

TitleStatusHype
CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference—0
Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots—0
Identification of Abnormality in Maize Plants From UAV Images Using Deep Learning Approaches—0
Positive-Unlabeled Node Classification with Structure-aware Graph Learning—0
An Event based Prediction Suffix Tree—0
Anomaly Detection of Command Shell Sessions based on DistilBERT: Unsupervised and Supervised Approaches—0
SigML++: Supervised Log Anomaly with Probabilistic Polynomial Approximation—0
A New Time Series Similarity Measure and Its Smart Grid Applications—0
VALD-GAN: video anomaly detection using latent discriminator augmented GAN—0
Open-Set Multivariate Time-Series Anomaly Detection—0
Spatially-resolved hyperlocal weather prediction and anomaly detection using IoT sensor networks and machine learning techniques—0
MST-GAT: A Multimodal Spatial-Temporal Graph Attention Network for Time Series Anomaly Detection—0
Data Drift Monitoring for Log Anomaly Detection Pipelines—0
Leveraging Large Language Model for Automatic Evolving of Industrial Data-Centric R&D Cycle—0
Transparent Anomaly Detection via Concept-based Explanations—0
Model Selection of Anomaly Detectors in the Absence of Labeled Validation Data—0
Predictive Maintenance Model Based on Anomaly Detection in Induction Motors: A Machine Learning Approach Using Real-Time IoT Data—0
DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time Series Anomaly Detection—0
Histogram- and Diffusion-Based Medical Out-of-Distribution Detection—0
Electrical Grid Anomaly Detection via Tensor Decomposition—0
A Supervised Embedding and Clustering Anomaly Detection method for classification of Mobile Network Faults—0
Assessing the Impact of a Supervised Classification Filter on Flow-based Hybrid Network Anomaly DetectionCode0
AnoDODE: Anomaly Detection with Diffusion ODE—0
Self-Discriminative Modeling for Anomalous Graph Detection—0
Multiscale Fusion for Abnormality Detection and Localization of Distributed Parameter Systems—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