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 3351–3375 of 4856 papers

TitleStatusHype
An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning—0
Disentangling Physical Parameters for Anomalous Sound Detection Under Domain Shifts—0
Variation and generality in encoding of syntactic anomaly information in sentence embeddings—0
Through-Foliage Tracking with Airborne Optical Sectioning—0
Online-compatible Unsupervised Non-resonant Anomaly DetectionCode0
Improving Novelty Detection using the Reconstructions of Nearest NeighboursCode0
Exploiting the Power of Levenberg-Marquardt Optimizer with Anomaly Detection in Time Series—0
Cross-Layered Distributed Data-driven Framework For Enhanced Smart Grid Cyber-Physical Security—0
A Novel Data Pre-processing Technique: Making Data Mining Robust to Different Units and Scales of Measurement—0
A Deep Learning Generative Model Approach for Image Synthesis of Plant Leaves—0
CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms—0
Towards Smart Monitored AM: Open Source in-Situ Layer-wise 3D Printing Image Anomaly Detection Using Histograms of Oriented Gradients and a Physics-Based Rendering Engine—0
A Personalized Federated Learning Algorithm: an Application in Anomaly Detection—0
Automated, real-time hospital ICU emergency signaling: A field-level implementation—0
A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods—0
A Modified Dynamic Time Warping (MDTW) Approach and Innovative Average Non-Self Match Distance (ANSD) Method for Anomaly Detection in ECG Recordings—0
Semantic Novelty Detection in Natural Language DescriptionsCode0
TADPOLE: Task ADapted Pre-Training via AnOmaLy DEtection—0
Markus Thill Temporal convolutional autoencoder for unsupervised anomaly detection in time series—0
Boosting Anomaly Detection Using Unsupervised Diverse Test-Time AugmentationCode0
PEDENet: Image Anomaly Localization via Patch Embedding and Density Estimation—0
Real-Time Detection of Anomalies in Large-Scale Transient Surveys—0
Evaluation of an Anomaly Detector for Routers using Parameterizable Malware in an IoT Ecosystem—0
Multi-Class Anomaly Detection—0
Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data ContaminationCode0
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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