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 4101–4125 of 4856 papers

TitleStatusHype
Regularized Cycle Consistent Generative Adversarial Network for Anomaly Detection—0
Neighborhood Structure Assisted Non-negative Matrix Factorization and its Application in Unsupervised Point-wise Anomaly Detection—0
Simulation Assisted Likelihood-free Anomaly DetectionCode0
Anomaly Detection with Density EstimationCode0
Unsupervised Learning of the Set of Local Maxima—0
Unsupervised Distribution Learning for Lunar Surface Anomaly Detection—0
Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift—0
Deep learning achieves perfect anomaly detection on 108,308 retinal images including unlearned diseasesCode0
Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing—0
Granular Learning with Deep Generative Models using Highly Contaminated Data—0
Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging—0
Characterizing Missing Information in Deep Networks Using Backpropagated Gradients—0
Integrative Tensor-based Anomaly Detection System For Satellites—0
KNN-Based Automatic Cropping for Improved Threat Object Recognition in X-Ray Security Images—0
A general anomaly detection framework for fleet-based condition monitoring of machines—0
Graph Embedded Pose Clustering for Anomaly DetectionCode0
History-based Anomaly Detector: an Adversarial Approach to Anomaly Detection—0
Unsupervised Representation Learning by Predicting Random DistancesCode0
Data Augmentation by AutoEncoders for Unsupervised Anomaly Detection—0
AEGR: A simple approach to gradient reversal in autoencoders for network anomaly detection—0
NFAD: Fixing anomaly detection using normalizing flowsCode0
Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks—0
Probabilistic Software Modeling: A Data-driven Paradigm for Software Analysis—0
MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking—0
An Unsupervised Framework for Anomaly Detection in a Water Treatment System.Code0
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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