SOTAVerified

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 4601–4625 of 4856 papers

TitleStatusHype
One-class Collective Anomaly Detection based on Long Short-Term Memory Recurrent Neural Networks—0
Anomaly detection in wide area network mesh using two machine learning anomaly detection algorithms—0
A Theoretical Investigation of Graph Degree as an Unsupervised Normality Measure—0
Learning Networks from Random Walk-Based Node SimilaritiesCode0
ECG Signal Preprocessing and SVM Classifier-Based Abnormality Detection in Remote Healthcare Applications—0
WEAC: Word embeddings for anomaly classification from event logs—0
Learning Deep Features for One-Class ClassificationCode0
Detecting abnormal events in video using Narrowed Normality Clusters—0
Paranom: A Parallel Anomaly Dataset Generator—0
Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection—0
An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videosCode0
Precision and Recall for Range-Based Anomaly Detection—0
Anomaly Detection with Generative Adversarial Networks—0
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly DetectionCode0
End-to-End Abnormality Detection in Medical Imaging—0
Unsupervised Adversarial Anomaly Detection using One-Class Support Vector Machines—0
Bayesian Time Series Forecasting with Change Point and Anomaly Detection—0
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
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
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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