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 43014325 of 4856 papers

TitleStatusHype
Devil in the Detail: Attack Scenarios in Industrial Applications0
Online Collection and Forecasting of Resource Utilization in Large-Scale Distributed Systems0
Learning Ensembles of Anomaly Detectors on Synthetic Data0
Semantic Analysis of Traffic Camera Data: Topic Signal Extraction and Anomalous Event Detection0
Online Multivariate Anomaly Detection and Localization for High-dimensional Settings0
Finding Rats in Cats: Detecting Stealthy Attacks using Group Anomaly Detection0
Which principal components are most sensitive to distributional changes?0
Using Bursty Announcements for Detecting BGP Routing Anomalies0
Visual Analytics of Anomalous User Behaviors: A Survey0
Online Anomaly Detection with Sparse Gaussian Processes0
Attack and Anomaly Detection in IoT Sensors in IoT Sites Using Machine Learning Approaches0
Inexact Block Coordinate Descent Algorithms for Nonsmooth Nonconvex OptimizationCode0
Anomaly Detection in Images0
1D Convolutional Neural Networks and Applications: A Survey0
Deep Anomaly Detection on Attributed NetworksCode1
A Multi-modal one-class generative adversarial network for anomaly detection in manufacturing0
UaiNets: From Unsupervised to Active Deep Anomaly Detection0
EnGAN: Latent Space MCMC and Maximum Entropy Generators for Energy-based Models0
Consistency-based anomaly detection with adaptive multiple-hypotheses predictionsCode0
Anomaly Detection in Traffic Scenes via Spatial-aware Motion Reconstruction0
Exploring Information Centrality for Intrusion Detection in Large Networks0
Reducing Anomaly Detection in Images to Detection in Noise0
GAN Augmented Text Anomaly Detection with Sequences of Deep Statistics0
A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised0
Deep Representation Learning for Social Network Analysis0
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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