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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2976–3000 of 4856 papers

TitleStatusHype
Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Models—0
Unsupervised Detection of Lung Nodules in Chest Radiography Using Generative Adversarial Networks—0
Unsupervised Dictionary Learning for Anomaly Detection—0
Unsupervised Distance Metric Learning for Anomaly Detection Over Multivariate Time Series—0
Unsupervised Distribution Learning for Lunar Surface Anomaly Detection—0
Unsupervised Driver Behavior Profiling leveraging Recurrent Neural Networks—0
Unsupervised Ensemble Methods for Anomaly Detection in PLC-based Process Control—0
Unsupervised Event Outlier Detection in Continuous Time—0
Unsupervised Feature Construction for Anomaly Detection in Time Series -- An Evaluation—0
Unsupervised Industrial Anomaly Detection via Pattern Generative and Contrastive Networks—0
Unsupervised KPIs-Based Clustering of Jobs in HPC Data Centers—0
Unsupervised Learning Approach to Anomaly Detection in Gravitational Wave Data—0
Unsupervised Learning of Distributional Properties can Supplement Human Labeling and Increase Active Learning Efficiency in Anomaly Detection—0
Unsupervised Learning of Multi-level Structures for Anomaly Detection—0
Unsupervised Learning of the Set of Local Maxima—0
Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra—0
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges—0
Unsupervised Multimodal Fusion of In-process Sensor Data for Advanced Manufacturing Process Monitoring—0
Unsupervised Online Anomaly Detection On Irregularly Sampled Or Missing Valued Time-Series Data Using LSTM Networks—0
Unsupervised Prediction of Negative Health Events Ahead of Time—0
Unsupervised Recycled FPGA Detection Using Symmetry Analysis—0
Unsupervised Region-based Anomaly Detection in Brain MRI with Adversarial Image Inpainting—0
Unsupervised Representation Learning and Anomaly Detection in ECG Sequences—0
Unsupervised Discovery, Control, and Disentanglement of Semantic Attributes with Applications to Anomaly Detection—0
Unsupervised Surface Anomaly Detection with Diffusion Probabilistic Model—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