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 2551–2575 of 4856 papers

TitleStatusHype
A Transfer Learning Framework for Anomaly Detection Using Model of Normality—0
A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data—0
Attack-Agnostic Adversarial Detection—0
Attack and Anomaly Detection in IoT Sensors in IoT Sites Using Machine Learning Approaches—0
Attacking Face Recognition with T-shirts: Database, Vulnerability Assessment and Detection—0
AttackLLM: LLM-based Attack Pattern Generation for an Industrial Control System—0
Attack Rules: An Adversarial Approach to Generate Attacks for Industrial Control Systems using Machine Learning—0
Attention and Autoencoder Hybrid Model for Unsupervised Online Anomaly Detection—0
Attention-Based Self-Supervised Feature Learning for Security Data—0
Attention Boosted Autoencoder for Building Energy Anomaly Detection—0
Attentioned Convolutional LSTM InpaintingNetwork for Anomaly Detection in Videos—0
Attention Fusion Reverse Distillation for Multi-Lighting Image Anomaly Detection—0
Attention-GAN for Anomaly Detection: A Cutting-Edge Approach to Cybersecurity Threat Management—0
Attention Guided Anomaly Localization in Images—0
Attention-Guided Perturbation for Unsupervised Image Anomaly Detection—0
Attention Map-guided Two-stage Anomaly Detection using Hard Augmentation—0
Attention Modules Improve Modern Image-Level Anomaly Detection: A DifferNet Case Study—0
Attention to Patterns is all you need for Insider threat detection—0
Attire-Based Anomaly Detection in Restricted Areas Using YOLOv8 for Enhanced CCTV Security—0
A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories—0
A Typology of Data Anomalies—0
Audio-based Anomaly Detection in Industrial Machines Using Deep One-Class Support Vector Data Description—0
Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing—0
Auditing Keyword Queries Over Text Documents—0
Augmentation based unsupervised domain adaptation—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