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 2151–2175 of 4856 papers

TitleStatusHype
DROCC: Deep Robust One-Class Classification—0
A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis—0
Active anomaly detection based on deep one-class classification—0
Driving Anomaly Detection Using Conditional Generative Adversarial Network—0
Driver Age and Its Effect on Key Driving Metrics: Insights from Dynamic Vehicle Data—0
A Survey on Deep Learning Techniques for Video Anomaly Detection—0
Drifter: Efficient Online Feature Monitoring for Improved Data Integrity in Large-Scale Recommendation Systems—0
DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities—0
A Survey on Anomaly Detection for Technical Systems using LSTM Networks—0
Anomaly-Aware Semantic Segmentation via Style-Aligned OoD Augmentation—0
Adversarially learned anomaly detection for time series data—0
DPOAD: Differentially Private Outsourcing of Anomaly Detection through Iterative Sensitivity Learning—0
DPGIIL: Dirichlet Process-Deep Generative Model-Integrated Incremental Learning for Clustering in Transmissibility-based Online Structural Anomaly Detection—0
DoS and DDoS Mitigation Using Variational Autoencoders—0
A Survey of Time Series Anomaly Detection Methods in the AIOps Domain—0
Anomaly-Aware Semantic Segmentation by Leveraging Synthetic-Unknown Data—0
DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN—0
Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization—0
A Survey of Single-Scene Video Anomaly Detection—0
A Survey of Emerging Applications of Diffusion Probabilistic Models in MRI—0
Anomaly Awareness—0
Activation Learning by Local Competitions—0
Abnormal activity capture from passenger flow of elevator based on unsupervised learning and fine-grained multi-label recognition—0
Recent Advances in Diffusion Models for Hyperspectral Image Processing and Analysis: A Review—0
Domain-Generalized Textured Surface Anomaly 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