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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

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Showing 4401–4450 of 4856 papers

TitleStatusHype
WaveletAE: A Wavelet-enhanced Autoencoder for Wind Turbine Blade Icing DetectionCode0
A Probabilistic Framework to Node-level Anomaly Detection in Communication Networks—0
BINet: Multi-perspective Business Process Anomaly ClassificationCode0
Bounded Fuzzy Possibilistic Method—0
Dictionary learning approach to monitoring of wind turbine drivetrain bearings—0
Experiment-based detection of service disruption attacks in optical networks using data analytics and unsupervised learningCode0
Automatic Hyperparameter Tuning Method for Local Outlier Factor, with Applications to Anomaly DetectionCode0
Unsupervised Prediction of Negative Health Events Ahead of Time—0
Securing Fog-to-Things Environment Using Intrusion Detection System Based On Ensemble Learning—0
f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial NetworksCode0
Anomaly Locality in Video Surveillance—0
Heartbeat Anomaly Detection using Adversarial OversamplingCode0
Generalization of feature embeddings transferred from different video anomaly detection domains—0
Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly DetectionCode0
Maximum Entropy Generators for Energy-Based ModelsCode0
Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey—0
Anomaly detection in the dynamics of web and social networksCode0
Robust Anomaly Detection in Images using Adversarial Autoencoders—0
Background subtraction on depth videos with convolutional neural networks—0
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial NetworksCode0
CFOF: A Concentration Free Measure for Anomaly Detection—0
A Machine-Synesthetic Approach To DDoS Network Attack Detection—0
Deep Learning for Anomaly Detection: A SurveyCode0
Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization—0
Autoencoders and Generative Adversarial Networks for Imbalanced Sequence ClassificationCode0
Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions—0
An Evaluation of Methods for Real-Time Anomaly Detection using Force Measurements from the Turning ProcessCode0
Feedforward Neural Network for Time Series Anomaly Detection—0
Correlated Anomaly Detection from Large Streaming Data—0
DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time SeriesCode0
Video Trajectory Classification and Anomaly Detection Using Hybrid CNN-VAECode0
Anomaly Detection and Interpretation using Multimodal Autoencoder and Sparse Optimization—0
Unsupervised Anomaly Detection in Energy Time Series Data Using Variational Recurrent Autoencoders with Attention—0
Mapper Comparison with Wasserstein MetricsCode0
AdaFlow: Domain-Adaptive Density Estimator with Application to Anomaly Detection and Unpaired Cross-Domain Translation—0
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection—0
Real-Time Anomaly Detection With HMOF Feature—0
Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT—0
Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in VideoCode0
Anomaly Generation using Generative Adversarial Networks in Host Based Intrusion Detection—0
Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks—0
Cyber Anomaly Detection Using Graph-node Role-dynamics—0
Anomaly detection with Wasserstein GAN—0
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time SeriesCode0
LSCP: Locally Selective Combination in Parallel Outlier EnsemblesCode0
Context Encoding Chest X-raysCode0
Inferring Networks From Random Walk-Based Node SimilaritiesCode0
Anomaly Detection for Network Connection Logs—0
ADSaS: Comprehensive Real-time Anomaly Detection System—0
Anomaly Detection Models for IoT Time Series Data—0
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All datasetsMVTec ADVisAMVTec LOCO ADOne-class CIFAR-10CUHK AvenueShanghaiTechFishyscapes L&FUCR Anomaly ArchiveMPDDBTADOne-class CIFAR-100UBnormal

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016—Unverified
2CPR-fast(TensorRT)FPS362—Unverified
3CPR(TensorRT)FPS130—Unverified
4GLASSDetection AUROC99.9—Unverified
5UniNetDetection AUROC99.9—Unverified
6HETMMDetection AUROC99.8—Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9DDADDetection AUROC99.8—Unverified
10PBASDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9—Unverified
5DDADDetection AUROC98.9—Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
7DiffusionADDetection AUROC98.8—Unverified
8GLASSDetection AUROC98.8—Unverified
9TransFusionDetection AUROC98.7—Unverified
10HETMMDetection AUROC98.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3—Unverified
2PSADAvg. Detection AUROC94.9—Unverified