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 44264450 of 4856 papers

TitleStatusHype
Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions0
An Evaluation of Methods for Real-Time Anomaly Detection using Force Measurements from the Turning ProcessCode0
Feedforward Neural Network for Time Series Anomaly Detection0
Correlated Anomaly Detection from Large Streaming Data0
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 Optimization0
Unsupervised Anomaly Detection in Energy Time Series Data Using Variational Recurrent Autoencoders with Attention0
Mapper Comparison with Wasserstein MetricsCode0
AdaFlow: Domain-Adaptive Density Estimator with Application to Anomaly Detection and Unpaired Cross-Domain Translation0
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection0
Real-Time Anomaly Detection With HMOF Feature0
Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT0
Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in VideoCode0
Anomaly Generation using Generative Adversarial Networks in Host Based Intrusion Detection0
Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks0
Cyber Anomaly Detection Using Graph-node Role-dynamics0
Anomaly detection with Wasserstein GAN0
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 Logs0
ADSaS: Comprehensive Real-time Anomaly Detection System0
Anomaly Detection Models for IoT Time Series Data0
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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