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 2301–2325 of 4856 papers

TitleStatusHype
ClusterLog: Clustering Logs for Effective Log-based Anomaly Detection—0
AltUB: Alternating Training Method to Update Base Distribution of Normalizing Flow for Anomaly Detection—0
HTTP2vec: Embedding of HTTP Requests for Detection of Anomalous Traffic—0
Human readable network troubleshooting based on anomaly detection and feature scoring—0
Clustering of Time Series Data with Prior Geographical Information—0
Clustering Driven Deep Autoencoder for Video Anomaly Detection—0
Anomaly Detection in XML-Structured SOAP Messages Using Tree-Based Association Rule Mining—0
Clustering-based Anomaly Detection for microservices—0
Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representations—0
Anomaly Detection in Wireless Sensor Networks—0
A Low-cost Strategic Monitoring Approach for Scalable and Interpretable Error Detection in Deep Neural Networks—0
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos—0
Anomaly detection in wide area network mesh using two machine learning anomaly detection algorithms—0
Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series—0
Anomaly detection in video with Bayesian nonparametrics—0
ALOJA-ML: A Framework for Automating Characterization and Knowledge Discovery in Hadoop Deployments—0
A Data Mining-Based Dynamical Anomaly Detection Method for Integrating with an Advance Metering System—0
ALOJA: A Framework for Benchmarking and Predictive Analytics in Big Data Deployments—0
CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms—0
Cloud-Based AI Systems: Leveraging Large Language Models for Intelligent Fault Detection and Autonomous Self-Healing—0
Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks—0
HR-Crime: Human-Related Anomaly Detection in Surveillance Videos—0
Anomaly Detection in Video Sequence With Appearance-Motion Correspondence—0
Deep Learning based pipeline for anomaly detection and quality enhancement in industrial binder jetting processes—0
CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection—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