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 10511075 of 4856 papers

TitleStatusHype
Adversarially Learned One-Class Classifier for Novelty DetectionCode0
A Survey on GANs for Anomaly DetectionCode0
Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory ModelsCode0
InQMAD: Incremental Quantum Measurement Anomaly DetectionCode0
Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quarkCode0
Integrating Quantum-Classical Attention in Patch Transformers for Enhanced Time Series ForecastingCode0
Inferring Networks From Random Walk-Based Node SimilaritiesCode0
InForecaster: Forecasting Influenza Hemagglutinin Mutations Through the Lens of Anomaly DetectionCode0
Industrial Image Anomaly Localization Based on Gaussian Clustering of Pretrained FeatureCode0
Inexact Block Coordinate Descent Algorithms for Nonsmooth Nonconvex OptimizationCode0
Input complexity and out-of-distribution detection with likelihood-based generative modelsCode0
Kinematic Detection of Anomalies in Human Trajectory DataCode0
Improving SIEM for Critical SCADA Water Infrastructures Using Machine LearningCode0
Improving Vision Anomaly Detection with the Guidance of Language ModalityCode0
Improving Novelty Detection using the Reconstructions of Nearest NeighboursCode0
Improved Anomaly Detection by Using the Attention-Based Isolation ForestCode0
Improved Anomaly Detection through Conditional Latent Space VAE EnsemblesCode0
A Subspace Method for Time Series Anomaly Detection in Cyber-Physical SystemsCode0
Importance Weighted Adversarial Discriminative Transfer for Anomaly DetectionCode0
Improved AutoEncoder with LSTM module and KL divergenceCode0
ISSTAD: Incremental Self-Supervised Learning Based on Transformer for Anomaly Detection and LocalizationCode0
A Study of Representational Properties of Unsupervised Anomaly Detection in Brain MRICode0
Adversarial Distillation of Bayesian Neural Network PosteriorsCode0
Imbalanced Graph-Level Anomaly Detection via Counterfactual Augmentation and Feature LearningCode0
Associative Knowledge Graphs for Efficient Sequence Storage and RetrievalCode0
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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