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 4101–4125 of 4856 papers

TitleStatusHype
DAS3D: Dual-modality Anomaly Synthesis for 3D Anomaly Detection—0
Data-Agnostic Face Image Synthesis Detection Using Bayesian CNNs—0
Data Anomaly Detection for Structural Health Monitoring of Bridges using Shapelet Transform—0
Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspection—0
Data Augmentation by AutoEncoders for Unsupervised Anomaly Detection—0
Data Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection—0
Data Drift Monitoring for Log Anomaly Detection Pipelines—0
Data-Driven Thermal Anomaly Detection in Large Battery Packs—0
Data-Driven Construction of Data Center Graph of Things for Anomaly Detection—0
Data Driven Diagnosis for Large Cyber-Physical-Systems with Minimal Prior Information—0
Data-driven Predictive Latency for 5G: A Theoretical and Experimental Analysis Using Network Measurements—0
Data-Driven Semi-Supervised Machine Learning with Safety Indicators for Abnormal Driving Behavior Detection—0
Data-driven Thermal Anomaly Detection for Batteries using Unsupervised Shape Clustering—0
Data-Driven Thermal Modelling for Anomaly Detection in Electric Vehicle Charging Stations—0
Data-Efficient and Interpretable Tabular Anomaly Detection—0
Data-Efficient Methods for Dialogue Systems—0
Data Quality Monitoring through Transfer Learning on Anomaly Detection for the Hadron Calorimeters—0
Data refinement for fully unsupervised visual inspection using pre-trained networks—0
Data Transformer for Anomalous Trajectory Detection—0
DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions—0
DCOR: Anomaly Detection in Attributed Networks via Dual Contrastive Learning Reconstruction—0
DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time Series Anomaly Detection—0
DDR-ID: Dual Deep Reconstruction Networks Based Image Decomposition for Anomaly Detection—0
Dealing with Imbalanced Classes in Bot-IoT Dataset—0
Decentralized Collaborative Learning Framework with External Privacy Leakage Analysis—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