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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3551–3575 of 4856 papers

TitleStatusHype
AURSAD: Universal Robot Screwdriving Anomaly Detection DatasetCode0
Decentralized Federated Learning Preserves Model and Data Privacy—0
Robust Attack Detection Approach for IIoT Using Ensemble Classifier—0
The Deep Radial Basis Function Data Descriptor (D-RBFDD) Network: A One-Class Neural Network for Anomaly Detection—0
[Re] Learning Memory Guided Normality for Anomaly DetectionCode1
Autoencoder-based Condition Monitoring and Anomaly Detection Method for Rotating Machines—0
Wisdom of the Contexts: Active Ensemble Learning for Contextual Anomaly Detection—0
Leveraging 3D Information in Unsupervised Brain MRI Segmentation—0
Blind Image Denoising and Inpainting Using Robust Hadamard AutoencodersCode0
Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude LearningCode1
Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey—0
Deep One-Class Classification via Interpolated Gaussian DescriptorCode1
Variational Autoencoders with a Structural Similarity Loss in Time of Flight MRAs—0
Flow Forecast: A deep learning for time series forecasting, classification, and anomaly detection framework built in PyTorch—0
Anomaly Detection Support Using Process Classification—0
Multi-Source Anomaly Detection in Distributed IT Systems—0
A Non-Parametric Subspace Analysis Approach with Application to Anomaly Detection Ensembles—0
Deep State Inference: Toward Behavioral Model Inference of Black-box Software SystemsCode0
Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly GeneratorsCode0
Anomaly Detection for Aggregated Data Using Multi-Graph Autoencoder—0
NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection—0
Copula Quadrant Similarity for Anomaly Scores—0
Detecting Log Anomalies with Multi-Head Attention (LAMA)—0
Adaptive Immunity for Software: Towards Autonomous Self-healing Systems—0
Industrial Image Anomaly Localization Based on Gaussian Clustering of Pretrained FeatureCode0
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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