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
Modeling Heterogeneous Statistical Patterns in High-dimensional Data by Adversarial Distributions: An Unsupervised Generative FrameworkCode0
Detecting Localized Density Anomalies in Multivariate Data via Coin-Flip StatisticsCode0
Detecting Irregular Network Activity with Adversarial Learning and Expert FeedbackCode0
Hyperedge Anomaly Detection with Hypergraph Neural NetworkCode0
Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encodersCode0
BRUNO: A Deep Recurrent Model for Exchangeable DataCode0
Supervised Contrastive Learning for Detecting Anomalous Driving Behaviours from Multimodal VideosCode0
AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on AnomaliesCode0
ANOMIX: A Simple yet Effective Hard Negative Generation via Mixing for Graph Anomaly DetectionCode0
Monte Carlo EM for Deep Time Series Anomaly DetectionCode0
HyperBrain: Anomaly Detection for Temporal Hypergraph Brain NetworksCode0
Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric RepresentationCode0
Hybrid Isolation Forest - Application to Intrusion DetectionCode0
Detecting Anomalous Events in Object-centric Business Processes via Graph Neural NetworksCode0
AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active LearningCode0
Hybrid Deep Neural Networks to Infer State Models of Black-Box SystemsCode0
Anomaly Detection with Variance Stabilized Density EstimationCode0
Breast Cancer Detection Using Convolutional Neural NetworksCode0
Detecting Anomalies in Image Classification by Means of Semantic RelationshipsCode0
Hybrid Deep Network for Anomaly DetectionCode0
Human Kinematics-inspired Skeleton-based Video Anomaly DetectionCode0
MSNM-Sensor: An Applied Network Monitoring Tool for Anomaly Detection in Complex Networks and SystemsCode0
robROSE: A robust approach for dealing with imbalanced data in fraud detectionCode0
HSS-IAD: A Heterogeneous Same-Sort Industrial Anomaly Detection DatasetCode0
How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?Code0
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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