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 4351–4400 of 4856 papers

TitleStatusHype
Segmentation-Based Deep-Learning Approach for Surface-Defect DetectionCode1
Ensemble Clustering for Graphs: Comparisons and ApplicationsCode0
Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly DetectionCode0
Learning Competitive and Discriminative Reconstructions for Anomaly Detection—0
Multi-Stage Fault Warning for Large Electric Grids Using Anomaly Detection and Machine Learning—0
GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly DetectionCode0
ADS-ME: Anomaly Detection System for Micro-expression Spotting—0
Learning Regularity in Skeleton Trajectories for Anomaly Detection in VideosCode0
Improving SIEM for Critical SCADA Water Infrastructures Using Machine LearningCode0
Explaining Anomalies Detected by Autoencoders Using SHAPCode1
adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detectionCode0
Unsupervised Traffic Accident Detection in First-Person VideosCode1
Deep Generative Design: Integration of Topology Optimization and Generative Models—0
Unsupervised Abnormality Detection through Mixed Structure Regularization (MSR) in Deep Sparse Autoencoders—0
Anomaly Detection for an E-commerce Pricing System—0
Towards Corner Case Detection for Autonomous Driving—0
Bayesian Anomaly Detection and Classification—0
Real-time PCG Anomaly Detection by Adaptive 1D Convolutional Neural Networks—0
Anomaly Detection with Adversarial Dual AutoencodersCode0
A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection—0
Twitch Plays Pokemon, Machine Learns Twitch: Unsupervised Context-Aware Anomaly Detection for Identifying Trolls in Streaming DataCode0
KINN: Incorporating Expert Knowledge in Neural Networks—0
Street Scene: A new dataset and evaluation protocol for video anomaly detection—0
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
BIVA: A Very Deep Hierarchy of Latent Variables for Generative ModelingCode1
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
f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial NetworksCode0
Securing Fog-to-Things Environment Using Intrusion Detection System Based On Ensemble Learning—0
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
One-Class Convolutional Neural NetworkCode1
Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey—0
Maximum Entropy Generators for Energy-Based ModelsCode0
Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active LearningCode1
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
Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization—0
Deep Learning for Anomaly Detection: A SurveyCode0
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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