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 3401–3425 of 4856 papers

TitleStatusHype
An Efficient Anomaly Detection Approach using Cube Sampling with Streaming Data—0
Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images—0
A Multi-Scale A Contrario method for Unsupervised Image Anomaly Detection—0
Stochastic tensor space feature theory with applications to robust machine learning—0
Probabilistic Robust Autoencoders for Outlier Detection—0
Real-Time Predictive Maintenance using Autoencoder Reconstruction and Anomaly Detection—0
Sequential Deep Learning Architectures for Anomaly Detection in Virtual Network Function Chains—0
ANOMALY DETECTION WITH FRAME-GROUP ATTENTION IN SURVEILLANCE VIDEOS—0
Anomaly Detection for Tabular Data with Internal Contrastive Learning—0
Fast Adaptive Anomaly Detection—0
STRIC: Stacked Residuals of Interpretable Components for Time Series Anomaly Detection—0
You May Need both Good-GAN and Bad-GAN for Anomaly Detection—0
A2B-GAN: Utilizing Unannotated Anomalous Images for Anomaly Detection in Medical Image Analysis—0
Patchwise Sparse Dictionary Learning from pre-trained Neural Network Activation Maps for Anomaly Detection in Images—0
Iterative Bilinear Temporal-Spectral Fusion for Unsupervised Representation Learning in Time Series—0
A multi-domain splitting framework for time-varying graph structure—0
S^3ADNet: Sequential Anomaly Detection with Pessimistic Contrastive Learning—0
No Shifted Augmentations (NSA): strong baselines for self-supervised Anomaly Detection—0
Anomaly Detection for High-Dimensional Data Using Large Deviations Principle—0
An Automated Data Engineering Pipeline for Anomaly Detection of IoT Sensor Data—0
Y-GAN: Learning Dual Data Representations for Efficient Anomaly Detection—0
Visual Anomaly Detection for Images: A Survey—0
An Energy Efficient Health Monitoring Approach with Wireless Body Area Networks—0
Distributed Deep Learning for Persistent Monitoring of agricultural Fields—0
Quantifying point cloud realism through adversarially learned latent representations—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