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

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Showing 4401–4450 of 4856 papers

TitleStatusHype
Autoencoders and Generative Adversarial Networks for Imbalanced Sequence ClassificationCode0
PyOD: A Python Toolbox for Scalable Outlier DetectionCode1
Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions—0
Feedforward Neural Network for Time Series Anomaly Detection—0
An Evaluation of Methods for Real-Time Anomaly Detection using Force Measurements from the Turning ProcessCode0
DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time SeriesCode0
Correlated Anomaly Detection from Large Streaming Data—0
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
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection—0
AdaFlow: Domain-Adaptive Density Estimator with Application to Anomaly Detection and Unpaired Cross-Domain Translation—0
Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT—0
Real-Time Anomaly Detection With HMOF Feature—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
Deep Anomaly Detection with Outlier ExposureCode1
Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks—0
Adversarially Learned Anomaly DetectionCode1
Anomaly detection with Wasserstein GAN—0
Cyber Anomaly Detection Using Graph-node Role-dynamics—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
Evaluating Bayesian Deep Learning Methods for Semantic SegmentationCode0
ADSaS: Comprehensive Real-time Anomaly Detection System—0
Anomaly Detection Models for IoT Time Series Data—0
Class Augmented Semi-Supervised Learning for Practical Clinical Analytics on Physiological Signals—0
A Machine-Learning Phase Classification Scheme for Anomaly Detection in Signals with Periodic Characteristics—0
Learning State Representations in Complex Systems with Multimodal Data—0
Attentioned Convolutional LSTM InpaintingNetwork for Anomaly Detection in Videos—0
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series DataCode0
Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos?—0
Probabilistic Graphs for Sensor Data-driven Modelling of Power Systems at Scale—0
‘Unexpected item in the bagging area’: Anomaly Detection in X-ray Security Images—0
Anomaly Detection using Deep Learning based Image Completion—0
The Trace Criterion for Kernel Bandwidth Selection for Support Vector Data Description—0
Anomaly Detection using Autoencoders in High Performance Computing SystemsCode0
Adversarial Learning-Based On-Line Anomaly Monitoring for Assured Autonomy—0
Estimation of Dimensions Contributing to Detected Anomalies with Variational Autoencoders—0
Anomaly Detection via Graphical LassoCode0
Extended Isolation ForestCode0
Anomaly Detection for imbalanced datasets with Deep Generative Models—0
Multiple profiles sensor-based monitoring and anomaly detection—0
Anomaly Detection With Multiple-Hypotheses PredictionsCode0
ADEPOS: Anomaly Detection based Power Saving for Predictive Maintenance using Edge Computing—0
Generative Neural Networks for Anomaly Detection in Crowded ScenesCode0
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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