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 3451–3475 of 4856 papers

TitleStatusHype
Anomaly Crossing: New Horizons for Video Anomaly Detection as Cross-domain Few-shot Learning—0
Anomaly Detection and Automated Labeling for Voter Registration File Changes—0
Anomaly detection and automatic labeling for solar cell quality inspection based on Generative Adversarial Network—0
Anomaly detection and classification for streaming data using PDEs—0
Anomaly Detection and Classification in Knowledge Graphs—0
Anomaly Detection And Classification In Time Series With Kervolutional Neural Networks—0
Anomaly Detection and Improvement of Clusters using Enhanced K-Means Algorithm—0
Anomaly Detection and Inlet Pressure Prediction in Water Distribution Systems Using Machine Learning—0
Anomaly Detection and Interpretation using Multimodal Autoencoder and Sparse Optimization—0
Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets—0
Anomaly Detection and Localisation using Mixed Graphical Models—0
Anomaly Detection and Localization based on Double Kernelized Scoring and Matrix Kernels—0
Anomaly Detection and Localization for Speech Deepfakes via Feature Pyramid Matching—0
Anomaly Detection and Localization in Crowded Scenes by Motion-field Shape Description and Similarity-based Statistical Learning—0
Anomaly Detection and Modeling in 802.11 Wireless Networks—0
Anomaly detection and motif discovery in symbolic representations of time series—0
Anomaly detection and regime searching in fitness-tracker data—0
Anomaly Detection and Removal Using Non-Stationary Gaussian Processes—0
Anomaly Detection and Radio-frequency Interference Classification with Unsupervised Learning in Narrowband Radio Technosignature Searches—0
Anomaly Detection and Sampling Cost Control via Hierarchical GANs—0
Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models—0
Anomaly Detection Based on Aggregation of Indicators—0
Anomaly Detection Based on Critical Paths for Deep Neural Networks—0
Anomaly Detection Based on Deep Learning Using Video for Prevention of Industrial Accidents—0
Anomaly Detection Based on Generalized Gaussian Distribution approach for Ultra-Wideband (UWB) Indoor Positioning System—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