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 2201–2225 of 4856 papers

TitleStatusHype
Meta-survey on outlier and anomaly detectionCode0
Detecting Contextual Network Anomalies with Graph Neural Networks—0
Unsupervised KPIs-Based Clustering of Jobs in HPC Data Centers—0
Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection—0
Physics-Informed Convolutional Autoencoder for Cyber Anomaly Detection in Power Distribution Grids—0
Adversarial Denoising Diffusion Model for Unsupervised Anomaly Detection—0
An unsupervised approach towards promptable defect segmentation in laser-based additive manufacturing by Segment Anything—0
Data-Driven Semi-Supervised Machine Learning with Safety Indicators for Abnormal Driving Behavior Detection—0
Detection and Imputation based Two-Stage Denoising Diffusion Power System Measurement Recovery under Cyber-Physical Uncertainties—0
A brief introduction to a framework named Multilevel Guidance-Exploration NetworkCode0
Intelligent Anomaly Detection for Lane Rendering Using Transformer with Self-Supervised Pre-Training and Customized Fine-Tuning—0
Anomaly Detection for Scalable Task Grouping in Reinforcement Learning-based RAN Optimization—0
How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection—0
A Unified Simulation Framework for Visual and Behavioral Fidelity in Crowd Analysis—0
A Self-Commissioning Edge Computing Method for Data-Driven Anomaly Detection in Power Electronic Systems—0
Pseudo Replay-based Class Continual Learning for Online New Category Anomaly Detection in Advanced Manufacturing—0
Learning Cortical Anomaly through Masked Encoding for Unsupervised Heterogeneity MappingCode0
Semi-Supervised Health Index Monitoring with Feature Generation and Fusion—0
EdgeConvFormer: Dynamic Graph CNN and Transformer based Anomaly Detection in Multivariate Time Series—0
OCGEC: One-class Graph Embedding Classification for DNN Backdoor DetectionCode0
Dynamic Erasing Network Based on Multi-Scale Temporal Features for Weakly Supervised Video Anomaly DetectionCode0
From Lab to Field: Real-World Evaluation of an AI-Driven Smart Video Solution to Enhance Community Safety—0
Few-Shot Anomaly Detection with Adversarial Loss for Robust Feature Representations—0
ADT: Agent-based Dynamic Thresholding for Anomaly Detection—0
Anomaly Detection Under Uncertainty Using Distributionally Robust Optimization Approach—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