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 2726–2750 of 4856 papers

TitleStatusHype
Deep Isolation Forest for Anomaly DetectionCode1
Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets—0
Hierarchical Conditional Variational Autoencoder Based Acoustic Anomaly Detection—0
Fast Deep Autoencoder for Federated learning—0
Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data StreamCode1
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly LocalizationCode1
Smart Meter Data Anomaly Detection using Variational Recurrent Autoencoders with Attention—0
A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems—0
Progressive GANomaly: Anomaly detection with progressively growing GANs—0
A Unified Model for Multi-class Anomaly DetectionCode2
Dual-Distribution Discrepancy for Anomaly Detection in Chest X-RaysCode1
Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensics—0
TSFEDL: A Python Library for Time Series Spatio-Temporal Feature Extraction and Prediction using Deep Learning (with Appendices on Detailed Network Architectures and Experimental Cases of Study)Code1
Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models—0
Position Encoding Enhanced Feature Mapping for Image Anomaly DetectionCode1
Early Abnormal Detection of Sewage Pipe Network: Bagging of Various Abnormal Detection Algorithms—0
[Reproducibility Report] Explainable Deep One-Class Classification—0
Perturbation Learning Based Anomaly Detection—0
Anomaly Detection with Test Time Augmentation and Consistency Evaluation—0
A Three-Stage Anomaly Detection Framework for Traffic VideosCode0
CAINNFlow: Convolutional block Attention modules and Invertible Neural Networks Flow for anomaly detection and localization tasks—0
Anomaly detection in surveillance videos using transformer based attention modelCode1
Invertible Neural Networks for Graph PredictionCode0
‘Am I the Bad One’? Predicting the Moral Judgement of the Crowd Using Pre–trained Language Models—0
Détection d’anomalies textuelles à base de l’ingénierie d’invite (Prompt Engineering-Based Text Anomaly Detection )—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