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 2526–2550 of 4856 papers

TitleStatusHype
A Survey on Anomaly Detection for Technical Systems using LSTM Networks—0
A Survey on Deep Learning Techniques for Video Anomaly Detection—0
A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis—0
A Survey on Embedding Dynamic Graphs—0
A Survey on Explainable Anomaly Detection—0
A Survey on Graph Representation Learning Methods—0
A Survey on Proactive Customer Care: Enabling Science and Steps to Realize it—0
A Survey on Social Media Anomaly Detection—0
A Survey on Time-Series Distance Measures—0
A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images—0
A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect—0
ADPS: Asymmetric Distillation Post-Segmentation for Image Anomaly Detection—0
A Synergy Scoring Filter for Unsupervised Anomaly Detection with Noisy Data—0
A systematic literature review of unsupervised learning algorithms for anomalous traffic detection based on flows—0
A Systematic Mapping Study in AIOps—0
A Systematic Review of Machine Learning in Sports Betting: Techniques, Challenges, and Future Directions—0
ATAC-Net: Zoomed view works better for Anomaly Detection—0
A Tale of Two Latent Flows: Learning Latent Space Normalizing Flow with Short-run Langevin Flow for Approximate Inference—0
A task of anomaly detection for a smart satellite Internet of things system—0
A Taxonomy of Anomalies in Log Data—0
A Temporal Anomaly Detection System for Vehicles utilizing Functional Working Groups and Sensor Channels—0
A Theoretical Framework for AI-driven data quality monitoring in high-volume data environments—0
A Theoretical Investigation of Graph Degree as an Unsupervised Normality Measure—0
A Time Series Multitask Framework Integrating a Large Language Model, Pre-Trained Time Series Model, and Knowledge Graph—0
Atom dimension adaptation for infinite set dictionary learning—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