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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 4476–4500 of 4856 papers

TitleStatusHype
Explainable multi-class anomaly detection on functional data—0
Explainable Transformer-Based Anomaly Detection for Internet of Things Security Check for updates—0
Explainable Unsupervised Anomaly Detection with Random Forest—0
Explained anomaly detection in text reviews: Can subjective scenarios be correctly evaluated?—0
Explaining Anomalies with Tensor Networks—0
Explaining Deep Learning-based Anomaly Detection in Energy Consumption Data by Focusing on Contextually Relevant Data—0
Explanation Method for Anomaly Detection on Mixed Numerical and Categorical Spaces—0
Exploiting Autoencoder's Weakness to Generate Pseudo Anomalies—0
Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection—0
Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT—0
Exploiting Point-Language Models with Dual-Prompts for 3D Anomaly Detection—0
Exploiting Spatial-temporal Correlations for Video Anomaly Detection—0
Exploiting the Power of Levenberg-Marquardt Optimizer with Anomaly Detection in Time Series—0
Exploring Diffusion Models for Unsupervised Video Anomaly Detection—0
Exploring Dual Model Knowledge Distillation for Anomaly Detection—0
Exploring Global and Local Information for Anomaly Detection with Normal Samples—0
Exploring Human Crowd Patterns and Categorization in Video Footage for Enhanced Security and Surveillance using Computer Vision and Machine Learning—0
Exploring Information Centrality for Intrusion Detection in Large Networks—0
Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection—0
Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection—0
Exploring the Design of Adaptation Protocols for Improved Generalization and Machine Learning Safety—0
Exploring the impact of Optimised Hyperparameters on Bi-LSTM-based Contextual Anomaly Detector—0
Exploring the Impact of Outlier Variability on Anomaly Detection Evaluation Metrics—0
Exploring the Intrinsic Probability Distribution for Hyperspectral Anomaly Detection—0
Exploring the Magnitude-Shape Plot Framework for Anomaly Detection in Crowded Video Scenes—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