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 3051–3075 of 4856 papers

TitleStatusHype
Intrinsic Anomaly Detection for Multi-Variate Time Series—0
Framing Algorithmic Recourse for Anomaly Detection—0
Generative Anomaly Detection for Time Series Datasets—0
DPOAD: Differentially Private Outsourcing of Anomaly Detection through Iterative Sensitivity Learning—0
Video Anomaly Detection via Prediction Network with Enhanced Spatio-Temporal Memory Exchange—0
Self-Supervised Training with Autoencoders for Visual Anomaly Detection—0
Open-source FPGA-ML codesign for the MLPerf Tiny BenchmarkCode0
Human-AI communication for human-human communication: Applying interpretable unsupervised anomaly detection to executive coaching—0
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed GraphsCode0
R2-AD2: Detecting Anomalies by Analysing the Raw GradientCode0
0/1 Deep Neural Networks via Block Coordinate Descent—0
3D unsupervised anomaly detection and localization through virtual multi-view projection and reconstruction: Clinical validation on low-dose chest computed tomographyCode0
Multi-Contextual Predictions with Vision Transformer for Video Anomaly Detection—0
Applications of Machine Learning to the Identification of Anomalous ER Claims—0
ARES: Locally Adaptive Reconstruction-based Anomaly ScoringCode0
Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets—0
Hierarchical Conditional Variational Autoencoder Based Acoustic Anomaly Detection—0
Fast Deep Autoencoder for Federated learning—0
A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems—0
Progressive GANomaly: Anomaly detection with progressively growing GANs—0
Smart Meter Data Anomaly Detection using Variational Recurrent Autoencoders with Attention—0
Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models—0
Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensics—0
[Reproducibility Report] Explainable Deep One-Class Classification—0
Anomaly Detection with Test Time Augmentation and Consistency Evaluation—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