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 1876–1900 of 4856 papers

TitleStatusHype
Enhanced Federated Anomaly Detection Through Autoencoders Using Summary Statistics-Based Thresholding—0
Augment to Detect Anomalies with Continuous Labelling—0
Enhanced Real-Time Threat Detection in 5G Networks: A Self-Attention RNN Autoencoder Approach for Spectral Intrusion Analysis—0
Enforcing Cybersecurity Constraints for LLM-driven Robot Agents for Online Transactions—0
Enhancing Abnormality Grounding for Vision Language Models with Knowledge Descriptions—0
Augmentation based unsupervised domain adaptation—0
Anomaly Detection Based on Generalized Gaussian Distribution approach for Ultra-Wideband (UWB) Indoor Positioning System—0
Enhancing Anomaly Detection via Generating Diversified and Hard-to-distinguish Synthetic Anomalies—0
Enhancing anomaly detection with topology-aware autoencoders—0
Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants—0
Enhancing Cybersecurity in IoT Networks: A Deep Learning Approach to Anomaly Detection—0
Energy-Efficient Classification for Anomaly Detection: The Wireless Channel as a Helper—0
Auditing Keyword Queries Over Text Documents—0
Energy-based Models for Video Anomaly Detection—0
Enhancing Multi-Class Anomaly Detection via Diffusion Refinement with Dual Conditioning—0
Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach—0
Enhancing Pothole Detection and Characterization: Integrated Segmentation and Depth Estimation in Road Anomaly Systems—0
Anomaly Detection Based on Deep Learning Using Video for Prevention of Industrial Accidents—0
Affine-Invariant Integrated Rank-Weighted Depth: Definition, Properties and Finite Sample Analysis—0
Energy-Based Anomaly Detection and Localization—0
Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing—0
End-to-End Convolutional Activation Anomaly Analysis for Anomaly Detection—0
End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection—0
Audio-based Anomaly Detection in Industrial Machines Using Deep One-Class Support Vector Data Description—0
Anomaly Detection Based on Critical Paths for Deep Neural Networks—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