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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 1801–1825 of 4856 papers

TitleStatusHype
E-commerce Anomaly Detection: A Bayesian Semi-Supervised Tensor Decomposition Approach using Natural Gradients—0
EdgeCentric: Anomaly Detection in Edge-Attributed Networks—0
Edge Conditional Node Update Graph Neural Network for Multi-variate Time Series Anomaly Detection—0
EdgeConvFormer: Dynamic Graph CNN and Transformer based Anomaly Detection in Multivariate Time Series—0
Edge-Enabled Anomaly Detection and Information Completion for Social Network Knowledge Graphs—0
Edge Storage Management Recipe with Zero-Shot Data Compression for Road Anomaly Detection—0
Exploiting Spatial-temporal Correlations for Video Anomaly Detection—0
Effective Abnormal Activity Detection on Multivariate Time Series Healthcare Data—0
Attack-Agnostic Adversarial Detection—0
Effectiveness Assessment of Recent Large Vision-Language Models—0
Anomaly Detection and Localization based on Double Kernelized Scoring and Matrix Kernels—0
Efficacy of Statistical and Artificial Intelligence-based False Information Cyberattack Detection Models for Connected Vehicles—0
Exploiting the Power of Levenberg-Marquardt Optimizer with Anomaly Detection in Time Series—0
Efficient and Scalable Structure Learning for Bayesian Networks: Algorithms and Applications—0
Exploring Global and Local Information for Anomaly Detection with Normal Samples—0
CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection—0
Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks—0
Efficient Anomaly Detection via Matrix Sketching—0
Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers—0
Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learning—0
Efficient Client Selection in Federated Learning—0
Efficient Consensus Model based on Proximal Gradient Method applied to Convolutional Sparse Problems—0
Anomaly Detection with the Voronoi Diagram Evolutionary Algorithm—0
A2Log: Attentive Augmented Log Anomaly Detection—0
Anomaly Anything: Promptable Unseen Visual Anomaly Generation—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