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 3026–3050 of 4856 papers

TitleStatusHype
Task-aware Similarity Learning for Event-triggered Time Series—0
Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation—0
SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection—0
Experiments on Anomaly Detection in Autonomous Driving by Forward-Backward Style Transfers—0
Data-Driven Thermal Modelling for Anomaly Detection in Electric Vehicle Charging Stations—0
A Benchmark dataset for predictive maintenance—0
Stochastic Functional Analysis and Multilevel Vector Field Anomaly Detection—0
On the Robustness and Anomaly Detection of Sparse Neural Networks—0
Generative Adversarial Networks and Other Generative Models—0
Signed Network Embedding with Application to Simultaneous Detection of Communities and Anomalies—0
ENCODE: Encoding NetFlows for Network Anomaly DetectionCode0
GCN-based Multi-task Representation Learning for Anomaly Detection in Attributed Networks—0
Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems—0
Red PANDA: Disambiguating Anomaly Detection by Removing Nuisance Factors—0
Leveraging Log Instructions in Log-based Anomaly DetectionCode0
BiPOCO: Bi-Directional Trajectory Prediction with Pose Constraints for Pedestrian Anomaly DetectionCode0
Transformer based Models for Unsupervised Anomaly Segmentation in Brain MR ImagesCode0
Anomaly Detection with Adversarially Learned Perturbations of Latent Space—0
Augment to Detect Anomalies with Continuous Labelling—0
A geometric framework for outlier detection in high-dimensional data—0
DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection—0
Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory ModelsCode0
A Causal Approach to Detecting Multivariate Time-series Anomalies and Root Causes—0
Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis—0
Learning Citywide Patterns of Life from Trajectory Monitoring—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