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 3026–3050 of 4856 papers

TitleStatusHype
Using Ensemble Classifiers to Detect Incipient Anomalies—0
Using Google Analytics to Support Cybersecurity Forensics—0
Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Model—0
Using Machine Learning for Anomaly Detection on a System-on-Chip under Gamma Radiation—0
Using Semantic Information for Defining and Detecting OOD Inputs—0
Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data—0
Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)—0
uTRAND: Unsupervised Anomaly Detection in Traffic Trajectories—0
VALD-GAN: video anomaly detection using latent discriminator augmented GAN—0
Variational autoencoder-based neural network model compression—0
Variational Autoencoders for Anomaly Detection in Respiratory Sounds—0
Variational Autoencoders with a Structural Similarity Loss in Time of Flight MRAs—0
Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series—0
Variation and generality in encoding of syntactic anomaly information in sentence embeddings—0
VERA: Explainable Video Anomaly Detection via Verbalized Learning of Vision-Language Models—0
Versatile Anomaly Detection with Outlier Preserving Distribution Mapping Autoencoders—0
VHetNets for AI and AI for VHetNets: An Anomaly Detection Case Study for Ubiquitous IoT—0
Video Anomaly Detection and Explanation via Large Language Models—0
Video Anomaly Detection and Localization Using Hierarchical Feature Representation and Gaussian Process Regression—0
Video Anomaly Detection and Localization via Gaussian Mixture Fully Convolutional Variational Autoencoder—0
Video Anomaly Detection By The Duality Of Normality-Granted Optical Flow—0
Video Anomaly Detection for Smart Surveillance—0
Video Anomaly Detection in 10 Years: A Survey and Outlook—0
Video Anomaly Detection using GAN—0
Video Anomaly Detection Using Pre-Trained Deep Convolutional Neural Nets and Context Mining—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