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 4026–4050 of 4856 papers

TitleStatusHype
Constructing a meta-learner for unsupervised anomaly detection—0
CONSULT: Contrastive Self-Supervised Learning for Few-shot Tumor Detection—0
Context-aware Domain Adaptation for Time Series Anomaly Detection—0
Context-Aware Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition—0
Context-aware TFL: A Universal Context-aware Contrastive Learning Framework for Temporal Forgery Localization—0
Context-Aware Trajectory Anomaly Detection—0
Context-aware Video Anomaly Detection in Long-Term Datasets—0
Context-Dependent Anomaly Detection for Low Altitude Traffic Surveillance—0
Context-Dependent Anomaly Detection with Knowledge Graph Embedding Models—0
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection—0
Contextual Affinity Distillation for Image Anomaly Detection—0
Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing—0
Continual Learning for Anomaly Detection in Surveillance Videos—0
Continual learning on deployment pipelines for Machine Learning Systems—0
Continuous GNN-based Anomaly Detection on Edge using Efficient Adaptive Knowledge Graph Learning—0
Continuous Test-time Domain Adaptation for Efficient Fault Detection under Evolving Operating Conditions—0
Learning Informative Health Indicators Through Unsupervised Contrastive Learning—0
Contrastive Learning for Time Series on Dynamic Graphs—0
Contrastive Predictive Coding for Anomaly Detection—0
Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Data—0
Contrastive-Regularized U-Net for Video Anomaly Detection—0
Contrastive Structured Anomaly Detection for Gaussian Graphical Models—0
Controllable RANSAC-based Anomaly Detection via Hypothesis Testing—0
Control theoretically explainable application of autoencoder methods to fault detection in nonlinear dynamic systems—0
Convolutional Autoencoders for Data Compression and Anomaly Detection in Small Satellite Technologies—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