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 16511675 of 4856 papers

TitleStatusHype
Efficient Model Monitoring for Quality Control in Cardiac Image SegmentationCode0
Anomaly detection in dynamic networksCode0
ENCODE: Encoding NetFlows for Network Anomaly DetectionCode0
Enhancing Time Series Forecasting with Fuzzy Attention-Integrated TransformersCode0
Early-Stage Anomaly Detection: A Study of Model Performance on Complete vs. Partial FlowsCode0
Effective and Efficient Representation Learning for Flight TrajectoriesCode0
Braced Fourier Continuation and Regression for Anomaly DetectionCode0
Action Sequence Augmentation for Early Graph-based Anomaly DetectionCode0
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health EpisodesCode0
Effect of Deep Transfer and Multi task Learning on Sperm Abnormality DetectionCode0
Bounding Boxes and Probabilistic Graphical Models: Video Anomaly Detection SimplifiedCode0
Adaptive NAD: Online and Self-adaptive Unsupervised Network Anomaly DetectorCode0
Adaptive Anomaly Detection in Chaotic Time Series with a Spatially Aware Echo State NetworkCode0
E-ABIN: an Explainable module for Anomaly detection in BIological NetworksCode0
(1 + )-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data SetsCode0
Dynamic Erasing Network Based on Multi-Scale Temporal Features for Weakly Supervised Video Anomaly DetectionCode0
Boosting Anomaly Detection Using Unsupervised Diverse Test-Time AugmentationCode0
Anomaly Detection in Cooperative Vehicle Perception Systems under Imperfect CommunicationCode0
Semi-Supervised Learning for Anomaly Traffic Detection via Bidirectional Normalizing FlowsCode0
A Hybrid Artificial Intelligence System for Automated EEG Background Analysis and Report GenerationCode0
Dual-Modality Vehicle Anomaly Detection via Bilateral Trajectory TracingCode0
Coupled-Space Attacks against Random-Walk-based Anomaly DetectionCode0
Enhancing Unsupervised Anomaly Detection with Score-Guided NetworkCode0
Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated LearningCode0
DTOR: Decision Tree Outlier Regressor to explain anomaliesCode0
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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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
7DDADDetection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9PBASDetection AUROC99.8Unverified
10HETMMDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4DDADDetection AUROC98.9Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
6INP-Former ViT-B (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