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

Showing 44764500 of 4856 papers

TitleStatusHype
Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning ApproachCode0
Real-Time Nonparametric Anomaly Detection in High-Dimensional SettingsCode0
Anomaly Detection with Generative Adversarial Networks for Multivariate Time SeriesCode0
Layerwise Perturbation-Based Adversarial Training for Hard Drive Health Degree Prediction0
Convolutional Graph Auto-encoder: A Deep Generative Neural Architecture for Probabilistic Spatio-temporal Solar Irradiance Forecasting0
Does Your Phone Know Your Touch?0
Coupled IGMM-GANs for deep multimodal anomaly detection in human mobility data0
Multi-level hypothesis testing for populations of heterogeneous networks0
Anomaly Detection in the Presence of Missing Values0
An Open Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology Abnormality Detection0
DeepFall -- Non-invasive Fall Detection with Deep Spatio-Temporal Convolutional AutoencodersCode0
AAD: Adaptive Anomaly Detection through traffic surveillance videos0
Surface Defect Saliency of Magnetic TileCode0
DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN0
Enhanced network anomaly detection based on deep neural networks0
Neuromorphic Architecture for the Hierarchical Temporal Memory0
Metric Learning for Novelty and Anomaly DetectionCode0
Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral imagesCode0
Image Anomalies: a Review and Synthesis of Detection Methods0
Robust Spectral Filtering and Anomaly Detection0
Anomaly Detection via Minimum Likelihood Generative Adversarial Networks0
Scalable Multi-Task Gaussian Process Tensor Regression for Normative Modeling of Structured Variation in Neuroimaging Data0
Call Detail Records Driven Anomaly Detection and Traffic Prediction in Mobile Cellular Networks0
Detector monitoring with artificial neural networks at the CMS experiment at the CERN Large Hadron ColliderCode0
Anomaly detection in static networks using egonets0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016Unverified
2CPR-fast(TensorRT)FPS362Unverified
3CPR(TensorRT)FPS130Unverified
4GLASSDetection AUROC99.9Unverified
5UniNetDetection AUROC99.9Unverified
6HETMMDetection AUROC99.8Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9DDADDetection AUROC99.8Unverified
10PBASDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
5DDADDetection AUROC98.9Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
7DiffusionADDetection AUROC98.8Unverified
8GLASSDetection AUROC98.8Unverified
9TransFusionDetection AUROC98.7Unverified
10HETMMDetection AUROC98.1Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3Unverified
2PSADAvg. Detection AUROC94.9Unverified