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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 4601–4650 of 4856 papers

TitleStatusHype
Anomaly Detection with Generative Adversarial Networks—0
Bayesian Time Series Forecasting with Change Point and Anomaly Detection—0
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly DetectionCode0
End-to-End Abnormality Detection in Medical Imaging—0
Future Frame Prediction for Anomaly Detection -- A New BaselineCode1
Overcomplete Frame Thresholding for Acoustic Scene Analysis—0
Squeezed Convolutional Variational AutoEncoder for Unsupervised Anomaly Detection in Edge Device Industrial Internet of Things—0
When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Time—0
Outlier Detection by Consistent Data Selection Method—0
MURA: Large Dataset for Abnormality Detection in Musculoskeletal RadiographsCode1
Recurrent Neural Network Language Models for Open Vocabulary Event-Level Cyber Anomaly DetectionCode0
QCBA: Improving Rule Classifiers Learned from Quantitative Data by Recovering Information Lost by DiscretisationCode0
Dropping Activation Outputs with Localized First-layer Deep Network for Enhancing User Privacy and Data Security—0
Spatio-Temporal Data Mining: A Survey of Problems and MethodsCode0
Image Captioning and Classification of Dangerous Situations—0
A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational AutoencoderCode2
Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networks—0
Inductive Representation Learning in Large Attributed Graphs—0
Bayesian Hypernetworks—0
On the Runtime-Efficacy Trade-off of Anomaly Detection Techniques for Real-Time Streaming Data—0
Machine Learning for Drug Overdose Surveillance—0
Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data StreamsCode0
A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN FrameworkCode0
The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantizationCode0
Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledge—0
Catching Anomalous Distributed Photovoltaics: An Edge-based Multi-modal Anomaly Detection—0
Practical Machine Learning for Cloud Intrusion Detection: Challenges and the Way Forward—0
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges—0
To Go or Not To Go? A Near Unsupervised Learning Approach For Robot Navigation—0
Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning—0
Ignoring Distractors in the Absence of Labels: Optimal Linear Projection to Remove False Positives During Anomaly Detection—0
Anomaly Detection in Hierarchical Data Streams under Unknown Models—0
Deep and Confident Prediction for Time Series at UberCode1
Medical Image Analysis using Convolutional Neural Networks: A Review—0
Abnormal Event Detection in Videos using Generative Adversarial Nets—0
Incorporating Feedback into Tree-based Anomaly DetectionCode1
Anomaly Detection: Review and preliminary Entropy method tests—0
Anomaly Detection in Wireless Sensor Networks—0
Bayesian Learning of Clique Tree Structure—0
Explaining Anomalies in Groups with Characterizing Subspace RulesCode0
Brain Abnormality Detection by Deep Convolutional Neural Network—0
Deep Learning for Medical Image Analysis—0
Energy-based Models for Video Anomaly Detection—0
Limiting the Reconstruction Capability of Generative Neural Network using Negative Learning—0
Sampling High Throughput Data for Anomaly Detection of Data-Base Activity—0
Anomaly Detection with Robust Deep AutoencodersCode0
Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data—0
Anomaly Detection on Graph Time Series—0
Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS DiagnosisCode0
Anomaly Detection by Robust Statistics—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