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 2601–2625 of 4856 papers

TitleStatusHype
Distance Metric-Based Learning with Interpolated Latent Features for Location Classification in Endoscopy Image and Video—0
Siamese Transition Masked Autoencoders as Uniform Unsupervised Visual Anomaly Detector—0
siForest: Detecting Network Anomalies with Set-Structured Isolation Forest—0
SIGMA: Single Interpolated Generative Model for Anomalies—0
SigML++: Supervised Log Anomaly with Probabilistic Polynomial Approximation—0
Signal Recovery on Graphs: Variation Minimization—0
Signature Isolation Forest—0
Signatures to help interpretability of anomalies—0
Signed Network Embedding with Application to Simultaneous Detection of Communities and Anomalies—0
SigSegment: A Signal-Based Segmentation Algorithm for Identifying Anomalous Driving Behaviours in Naturalistic Driving Videos—0
SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmark—0
Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification—0
Simulating Battery-Powered TinyML Systems Optimised using Reinforcement Learning in Image-Based Anomaly Detection—0
Simulating Malicious Attacks on VANETs for Connected and Autonomous Vehicle Cybersecurity: A Machine Learning Dataset—0
SincVAE: a New Approach to Improve Anomaly Detection on EEG Data Using SincNet and Variational Autoencoder—0
Single- and Multi-Agent Private Active Sensing: A Deep Neuroevolution Approach—0
SiTGRU: Single-Tunnelled Gated Recurrent Unit for Abnormality Detection—0
Size-Consistent Statistics for Anomaly Detection in Dynamic Networks—0
Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions—0
Skeleton-based human action evaluation using graph convolutional network for monitoring Alzheimer’s progression—0
Sketching Multidimensional Time Series for Fast Discord Mining—0
Sleep Analytics and Online Selective Anomaly Detection—0
Sleep Apnea and Respiratory Anomaly Detection from a Wearable Band and Oxygen Saturation—0
Sliced-Wasserstein Distance-based Data Selection—0
SlowFastVAD: Video Anomaly Detection via Integrating Simple Detector and RAG-Enhanced Vision-Language 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