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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 2076–2100 of 4856 papers

TitleStatusHype
Imbalanced Aircraft Data Anomaly Detection—0
A Probabilistic Transformation of Distance-Based OutliersCode0
Concurrent Misclassification and Out-of-Distribution Detection for Semantic Segmentation via Energy-Based Normalizing FlowCode1
Evaluation Strategy of Time-series Anomaly Detection with Decay Function—0
Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptation—0
Component-aware anomaly detection framework for adjustable and logical industrial visual inspectionCode1
A Dataset Fusion Algorithm for Generalised Anomaly Detection in Homogeneous Periodic Time Series Datasets—0
Voxel-wise classification for porosity investigation of additive manufactured parts with 3D unsupervised and (deeply) supervised neural networksCode0
Configurable Spatial-Temporal Hierarchical Analysis for Flexible Video Anomaly Detection—0
Anomaly Detection Dataset for Industrial Control Systems—0
Self-Supervised Anomaly Detection of Rogue Soil Moisture Sensors—0
AnomalyBERT: Self-Supervised Transformer for Time Series Anomaly Detection using Data Degradation SchemeCode1
Is AUC the best measure for practical comparison of anomaly detectors?Code0
CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimation—0
Efficient pattern-based anomaly detection in a network of multivariate devices—0
Weakly-Supervised Anomaly Detection in the Milky WayCode0
Advances on the classification of radio image cubes—0
Revisiting Graph Contrastive Learning for Anomaly DetectionCode0
In-situ Anomaly Detection in Additive Manufacturing with Graph Neural Networks—0
Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completionCode0
Correlation-Driven Multi-Level Multimodal Learning for Anomaly Detection on Multiple Energy SourcesCode0
Unsupervised anomaly detection algorithms on real-world data: how many do we need?Code1
Two-phase Dual COPOD Method for Anomaly Detection in Industrial Control System—0
Detecting Novelties with Empty Classes—0
Impact of Deep Learning Libraries on Online Adaptive Lightweight Time Series 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