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 4701–4725 of 4856 papers

TitleStatusHype
Robust training on approximated minimal-entropy set—0
Multilevel Anomaly Detection for Mixed Data—0
Statistical Inference Using Mean Shift Denoising—0
Towards a Theoretical Analysis of PCA for Heteroscedastic Data—0
Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection—0
A Discriminative Framework for Anomaly Detection in Large Videos—0
One-Class SVM with Privileged Information and its Application to Malware Detection—0
Conformalized Kernel Ridge Regression—0
Online Data Thinning via Multi-Subspace Tracking—0
A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories—0
Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes—0
The Semantic Knowledge Graph: A compact, auto-generated model for real-time traversal and ranking of any relationship within a domainCode0
Entity Embedding-based Anomaly Detection for Heterogeneous Categorical Events—0
Network Volume Anomaly Detection and Identification in Large-scale Networks based on Online Time-structured Traffic Tensor Tracking—0
Conformalized density- and distance-based anomaly detection in time-series data—0
Anomaly detection and classification for streaming data using PDEs—0
Machine Learning with Memristors via Thermodynamic RAM—0
Compressive Change Retrieval for Moving Object Detection—0
Self-Organising Maps in Computer Security—0
Size-Consistent Statistics for Anomaly Detection in Dynamic Networks—0
Putting Sarcasm Detection into Context: The Effects of Class Imbalance and Manual Labelling on Supervised Machine Classification of Twitter Conversations—0
Discriminative Deep Random Walk for Network Classification—0
Emotion-Based Crowd Representation for Abnormality Detection—0
Supervised Anomaly Detection in Uncertain Pseudoperiodic Data Streams—0
Anomaly Detection and Localisation using Mixed Graphical Models—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