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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 1276–1300 of 4856 papers

TitleStatusHype
Deep Graph Learning for Anomalous Citation Detection—0
A convolutional neural network of low complexity for tumor anomaly detection—0
An Input-to-State Safety Approach Towards Safe Control of a Class of Parabolic PDEs Under Disturbances—0
Deep Generative Models Strike Back! Improving Understanding and Evaluation in Light of Unmet Expectations for OoD Data—0
Advancing Cyber-Attack Detection in Power Systems: A Comparative Study of Machine Learning and Graph Neural Network Approaches—0
Deep Generative Model using Unregularized Score for Anomaly Detection with Heterogeneous Complexity—0
Are vision language models robust to uncertain inputs?—0
An Incremental Clustering Method for Anomaly Detection in Flight Data—0
A review on outlier/anomaly detection in time series data—0
A Review of Physics-Informed Machine Learning Methods with Applications to Condition Monitoring and Anomaly Detection—0
An Improvement of PAA on Trend-Based Approximation for Time Series—0
Advancing climate model interpretability: Feature attribution for Arctic melt anomalies—0
A Review of Open Source Software Tools for Time Series Analysis—0
A review of neural network algorithms and their applications in supercritical extraction—0
Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs—0
A Review of Machine Learning based Anomaly Detection Techniques—0
A Review of Computer Vision Methods in Network Security—0
An Explainable Deep Learning Framework for Brain Stroke and Tumor Progression via MRI Interpretation—0
A Contrario multi-scale anomaly detection method for industrial quality inspection—0
An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery—0
An Explainable Anomaly Detection Framework for Monitoring Depression and Anxiety Using Consumer Wearable Devices—0
Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos?—0
Advancing Video Anomaly Detection: A Concise Review and a New Dataset—0
ABBA: Adaptive Brownian bridge-based symbolic aggregation of time series—0
Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark—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