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 3176–3200 of 4856 papers

TitleStatusHype
A Causal-based Framework for Multimodal Multivariate Time Series Validation Enhanced by Unsupervised Deep Learning as an Enabler for Industry 4.0—0
Accurate and Efficient Two-Stage Gun Detection in Video—0
Accurate and fast anomaly detection in industrial processes and IoT environments—0
ACMamba: Fast Unsupervised Anomaly Detection via An Asymmetrical Consensus State Space Model—0
AcME-AD: Accelerated Model Explanations for Anomaly Detection—0
A Combination of Temporal Sequence Learning and Data Description for Anomaly-based NIDS—0
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection—0
A Comparative Study of Machine Learning Algorithms for Anomaly Detection in Industrial Environments: Performance and Environmental Impact—0
Feature Extraction for Novelty Detection in Network Traffic—0
A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis—0
A comparison of classical and variational autoencoders for anomaly detection—0
A Comparison of Deep Learning Architectures for Spacecraft Anomaly Detection—0
A Comparison of Event Representations in DEFT—0
A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised—0
A Comprehensive Augmentation Framework for Anomaly Detection—0
A comprehensive study of sparse codes on abnormality detection—0
A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions—0
A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems—0
A Comprehensive Survey of Transformers for Computer Vision—0
A Continual and Incremental Learning Approach for TinyML On-device Training Using Dataset Distillation and Model Size Adaption—0
A Contrario multi-scale anomaly detection method for industrial quality inspection—0
A convolutional neural network of low complexity for tumor anomaly detection—0
Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning—0
Acoustic anomaly detection via latent regularized gaussian mixture generative adversarial networks—0
Acoustic Leak Detection in Water 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