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 2426–2450 of 4856 papers

TitleStatusHype
Regularized Cycle Consistent Generative Adversarial Network for Anomaly Detection—0
Regularizing Attention Networks for Anomaly Detection in Visual Question Answering—0
Reimagining Anomalies: What If Anomalies Were Normal?—0
Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection—0
Removing Anomalies as Noises for Industrial Defect Localization—0
Removing Class Imbalance using Polarity-GAN: An Uncertainty Sampling Approach—0
RePAD2: Real-Time, Lightweight, and Adaptive Anomaly Detection for Open-Ended Time Series—0
RePAD: Real-time Proactive Anomaly Detection for Time Series—0
Representation Learning for Resource Usage Prediction—0
Representation Learning in Anomaly Detection: Successes, Limits and a Grand Challenge—0
Representing Timed Automata and Timing Anomalies of Cyber-Physical Production Systems in Knowledge Graphs—0
[Reproducibility Report] Explainable Deep One-Class Classification—0
ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series—0
RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers—0
Research and application of Transformer based anomaly detection model: A literature review—0
Research on Anomaly Detection Methods Based on Diffusion Models—0
Research on Cloud Platform Network Traffic Monitoring and Anomaly Detection System based on Large Language Models—0
Research on Dynamic Data Flow Anomaly Detection based on Machine Learning—0
Residual ANODE—0
Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End—0
Resilient VAE: Unsupervised Anomaly Detection at the SLAC Linac Coherent Light Source—0
Resonant Anomaly Detection with Multiple Reference Datasets—0
Respiratory Anomaly Detection using Reflected Infrared Light-wave Signals—0
Restricted Generative Projection for One-Class Classification and Anomaly Detection—0
ReSynthDetect: A Fundus Anomaly Detection Network with Reconstruction and Synthetic Features—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