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 2676–2700 of 4856 papers

TitleStatusHype
Automated visual inspection of CMS HGCAL silicon sensor surface using an ensemble of a deep convolutional autoencoder and classifier—0
Updated version: A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency—0
Synthetic Pseudo Anomalies for Unsupervised Video Anomaly Detection: A Simple yet Efficient Framework based on Masked Autoencoder—0
Multi-level Memory-augmented Appearance-Motion Correspondence Framework for Video Anomaly Detection—0
Learning Representation for Anomaly Detection of Vehicle Trajectories—0
Understanding the Challenges and Opportunities of Pose-based Anomaly Detection—0
Region and Spatial Aware Anomaly Detection for Fundus Images—0
TinyAD: Memory-efficient anomaly detection for time series data in Industrial IoT—0
Time series anomaly detection with reconstruction-based state-space modelsCode0
Memory Maps for Video Object Detection and Tracking on UAVs—0
PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow—0
Achieving Counterfactual Fairness for Anomaly DetectionCode0
Seq-HyGAN: Sequence Classification via Hypergraph Attention Network—0
Unsupervised Recycled FPGA Detection Using Symmetry Analysis—0
Early Warning Signals of Social Instabilities in Twitter Data—0
Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption—0
Multi-Task Self-Supervised Time-Series Representation Learning—0
Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time SeriesCode0
CADeSH: Collaborative Anomaly Detection for Smart Homes—0
Blockchain-enabled fraud discovery through abnormal smart contract detection on Ethereum—0
RePAD2: Real-Time, Lightweight, and Adaptive Anomaly Detection for Open-Ended Time Series—0
Implementing Active Learning in Cybersecurity: Detecting Anomalies in Redacted Emails—0
Texture-Based Input Feature Selection for Action Recognition—0
Time Series Anomaly Detection in Smart Homes: A Deep Learning Approach—0
Unsupervised Video Anomaly Detection for Stereotypical Behaviours in Autism—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