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 2501–2525 of 4856 papers

TitleStatusHype
Runtime Anomaly Detection for Drones: An Integrated Rule-Mining and Unsupervised-Learning Approach—0
RW-NSGCN: A Robust Approach to Structural Attacks via Negative Sampling—0
RX-ADS: Interpretable Anomaly Detection using Adversarial ML for Electric Vehicle CAN data—0
S2DEVFMAP: Self-Supervised Learning Framework with Dual Ensemble Voting Fusion for Maximizing Anomaly Prediction in Timeseries—0
S^3ADNet: Sequential Anomaly Detection with Pessimistic Contrastive Learning—0
SAFELearning: Enable Backdoor Detectability In Federated Learning With Secure Aggregation—0
SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection—0
SALAD: Self-Adaptive Lightweight Anomaly Detection for Real-time Recurrent Time Series—0
SaliencyCut: Augmenting Plausible Anomalies for Anomaly Detection—0
SALINA: Towards Sustainable Live Sonar Analytics in Wild Ecosystems—0
SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection—0
Sampling High Throughput Data for Anomaly Detection of Data-Base Activity—0
Sampling - Variational Auto Encoder - Ensemble: In the Quest of Explainable Artificial Intelligence—0
Satellite Anomaly Detection Using Variance Based Genetic Ensemble of Neural Networks—0
SCADE: Scalable Framework for Anomaly Detection in High-Performance System—0
Log-based Anomaly Detection based on EVT Theory with feedback—0
Scalable and Decentralized Algorithms for Anomaly Detection via Learning-Based Controlled Sensing—0
Scalable Anomaly Detection in Large Homogenous Populations—0
Scalable Multi-Task Gaussian Process Tensor Regression for Normative Modeling of Structured Variation in Neuroimaging Data—0
SCALA: Sparsification-based Contrastive Learning for Anomaly Detection on Attributed Networks—0
Scaling New Peaks: A Viewership-centric Approach to Automated Content Curation—0
SCANIA Component X Dataset: A Real-World Multivariate Time Series Dataset for Predictive Maintenance—0
Scene Change Detection Using Multiscale Cascade Residual Convolutional Neural Networks—0
SCNet: A Generalized Attention-based Model for Crack Fault Segmentation—0
Score Combining for Contrastive OOD Detection—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