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 44014425 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 Networks0
BINet: Multi-perspective Business Process Anomaly ClassificationCode0
Bounded Fuzzy Possibilistic Method0
Dictionary learning approach to monitoring of wind turbine drivetrain bearings0
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 Time0
Securing Fog-to-Things Environment Using Intrusion Detection System Based On Ensemble Learning0
f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial NetworksCode0
Anomaly Locality in Video Surveillance0
Heartbeat Anomaly Detection using Adversarial OversamplingCode0
Generalization of feature embeddings transferred from different video anomaly detection domains0
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 Survey0
Anomaly detection in the dynamics of web and social networksCode0
Robust Anomaly Detection in Images using Adversarial Autoencoders0
Background subtraction on depth videos with convolutional neural networks0
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial NetworksCode0
CFOF: A Concentration Free Measure for Anomaly Detection0
A Machine-Synesthetic Approach To DDoS Network Attack Detection0
Deep Learning for Anomaly Detection: A SurveyCode0
Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization0
Autoencoders and Generative Adversarial Networks for Imbalanced Sequence ClassificationCode0
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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