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 2101–2125 of 4856 papers

TitleStatusHype
SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classification—0
POET: A Self-learning Framework for PROFINET Industrial Operations Behaviour—0
Model-Based Monitoring and State Estimation for Digital Twins: The Kalman Filter—0
Synthetic Aperture Anomaly Imaging—0
Real-time Safety Assessment of Dynamic Systems in Non-stationary Environments: A Review of Methods and TechniquesCode0
Blockchain Large Language Models—0
MoniLog: An Automated Log-Based Anomaly Detection System for Cloud Computing Infrastructures—0
Detecting Socially Abnormal Highway Driving Behaviors via Recurrent Graph Attention NetworksCode1
Constructing a meta-learner for unsupervised anomaly detection—0
Reconstruction-based LSTM-Autoencoder for Anomaly-based DDoS Attack Detection over Multivariate Time-Series Data—0
Interactive System-wise Anomaly Detection—0
An Attention Free Conditional Autoencoder For Anomaly Detection in Cryptocurrencies—0
Weakly Supervised Detection of Baby Cry—0
Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection—0
Graph Neural Network-Based Anomaly Detection for River Network SystemsCode1
Long-term Forecasting with TiDE: Time-series Dense EncoderCode5
One-Class SVM on siamese neural network latent space for Unsupervised Anomaly Detection on brain MRI White Matter Hyperintensities—0
Spot The Odd One Out: Regularized Complete Cycle Consistent Anomaly Detector GANCode1
Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection—0
Context-aware Domain Adaptation for Time Series Anomaly Detection—0
Few-shot Weakly-supervised Cybersecurity Anomaly Detection—0
Signal Novelty Detection as an Intrinsic Reward for RoboticsCode0
Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection—0
SigSegment: A Signal-Based Segmentation Algorithm for Identifying Anomalous Driving Behaviours in Naturalistic Driving Videos—0
Brain Structure Ages -- A new biomarker for multi-disease classification—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