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 44014425 of 4856 papers

TitleStatusHype
Rule Extraction in Unsupervised Anomaly Detection for Model Explainability: Application to OneClass SVMCode0
UN-AVOIDS: Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant Detection ScoringCode0
Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHCCode0
Anomaly Detection using Principles of Human PerceptionCode0
BiPOCO: Bi-Directional Trajectory Prediction with Pose Constraints for Pedestrian Anomaly DetectionCode0
Graph Laplacian for Image Anomaly DetectionCode0
Graph Fairing Convolutional Networks for Anomaly DetectionCode0
An anomaly detection approach for backdoored neural networks: face recognition as a case studyCode0
SADDE: Semi-supervised Anomaly Detection with Dependable ExplanationsCode0
Multivariate Time Series Anomaly Detection: Fancy Algorithms and Flawed Evaluation MethodologyCode0
Graph Embedded Pose Clustering for Anomaly DetectionCode0
Multivariate Time Series Anomaly Detection using DiffGAN ModelCode0
Label-based Graph Augmentation with Metapath for Graph Anomaly DetectionCode0
TeVAE: A Variational Autoencoder Approach for Discrete Online Anomaly Detection in Variable-state Multivariate Time-series DataCode0
BINet: Multi-perspective Business Process Anomaly ClassificationCode0
SAIFE: Unsupervised Wireless Spectrum Anomaly Detection with Interpretable FeaturesCode0
Multi-view Deep One-class Classification: A Systematic ExplorationCode0
ADKGD: Anomaly Detection in Knowledge Graphs with Dual-Channel TrainingCode0
VegaEdge: Edge AI Confluence Anomaly Detection for Real-Time Highway IoT-ApplicationsCode0
Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly DetectionCode0
GradStop: Exploring Training Dynamics in Unsupervised Outlier Detection through GradientCode0
Good Practices and A Strong Baseline for Traffic Anomaly DetectionCode0
Mutually exciting point process graphs for modelling dynamic networksCode0
GLADMamba: Unsupervised Graph-Level Anomaly Detection Powered by Selective State Space ModelCode0
GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario DetectionCode0
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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