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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 2326–2350 of 4856 papers

TitleStatusHype
Remembering Normality: Memory-guided Knowledge Distillation for Unsupervised Anomaly DetectionCode1
Removing Anomalies as Noises for Industrial Defect Localization—0
Unsupervised Surface Anomaly Detection with Diffusion Probabilistic Model—0
Video Anomaly Detection via Sequentially Learning Multiple Pretext Tasks—0
Template-guided Hierarchical Feature Restoration for Anomaly Detection—0
Inter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class ClassificationCode1
Feature Prediction Diffusion Model for Video Anomaly Detection—0
FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction—0
Anomaly Detection using Score-based Perturbation ResilienceCode1
Look Around for Anomalies: Weakly-Supervised Anomaly Detection via Context-Motion Relational Learning—0
OmniAL: A Unified CNN Framework for Unsupervised Anomaly Localization—0
Generating Anomalies for Video Anomaly Detection With Prompt-Based Feature Mapping—0
Revisiting Reverse Distillation for Anomaly DetectionCode1
A principled distributional approach to trajectory similarity measurement—0
A Latent Space Correlation-Aware Autoencoder for Anomaly Detection in Skewed Data—0
Exploring the Use of Data-Driven Approaches for Anomaly Detection in the Internet of Things (IoT) Environment—0
Application Of ADNN For Background Subtraction In Smart Surveillance System—0
Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions—0
Time series Forecasting to detect anomalous behaviours in Multiphase Flow Meters—0
Label-Efficient Interactive Time-Series Anomaly Detection—0
PRISM: Privacy Preserving Healthcare Internet of Things Security Management—0
AER: Auto-Encoder with Regression for Time Series Anomaly DetectionCode3
Anomaly detection in laser-guided vehicles' batteries: a case study—0
Application of Unsupervised Domain Adaptation for Structural MRI Analysis—0
A Novel Self-Supervised Learning-Based Anomaly Node Detection Method Based on an Autoencoder in Wireless Sensor NetworksCode0
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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