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 851–875 of 4856 papers

TitleStatusHype
Combining Switching Mechanism with Re-Initialization and Anomaly Detection for Resiliency of Cyber-Physical Systems—0
Vision-Language Models Assisted Unsupervised Video Anomaly Detection—0
Demystifying and Extracting Fault-indicating Information from Logs for Failure DiagnosisCode1
Towards the Discovery of Down Syndrome Brain Biomarkers Using Generative Models—0
MeLIAD: Interpretable Few-Shot Anomaly Detection with Metric Learning and Entropy-based Scoring—0
Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection FrameworkCode2
Towards Unbiased Evaluation of Time-series Anomaly DetectorCode0
Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space—0
Investigation on domain adaptation of additive manufacturing monitoring systems to enhance digital twin reusability—0
Cloudy with a Chance of Anomalies: Dynamic Graph Neural Network for Early Detection of Cloud Services' User AnomaliesCode0
PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities—0
Constraint Guided AutoEncoders for Joint Optimization of Condition Indicator Estimation and Anomaly Detection in Machine Condition Monitoring—0
Unsupervised Hybrid framework for ANomaly Detection (HAND) -- applied to Screening MammogramCode0
Adaptive Anomaly Detection in Network Flows with Low-Rank Tensor Decompositions and Deep UnrollingCode0
Multimodal Attention-Enhanced Feature Fusion-based Weekly Supervised Anomaly Violence Detection—0
Fair Anomaly Detection For Imbalanced Groups—0
Enhancing Anomaly Detection via Generating Diversified and Hard-to-distinguish Synthetic Anomalies—0
Deep Graph Anomaly Detection: A Survey and New PerspectivesCode3
Abnormal Event Detection In Videos Using Deep Embedding—0
Towards Multi-view Graph Anomaly Detection with Similarity-Guided Contrastive Clustering—0
OML-AD: Online Machine Learning for Anomaly Detection in Time Series DataCode0
Matrix Profile for Anomaly Detection on Multidimensional Time Series—0
Optimal Classification-based Anomaly Detection with Neural Networks: Theory and PracticeCode0
A Survey of Anomaly Detection in In-Vehicle Networks—0
Ensemble Methods for Sequence Classification with Hidden Markov Models—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