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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4051–4075 of 4856 papers

TitleStatusHype
Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems—0
A clustering approach to time series forecasting using neural networks: A comparative study on distance-based vs. feature-based clustering methodsCode0
Detection of Thin Boundaries between Different Types of Anomalies in Outlier Detection using Enhanced Neural Networks—0
Learning a distance function with a Siamese network to localize anomalies in videos—0
RePAD: Real-time Proactive Anomaly Detection for Time Series—0
Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification—0
Universal Data Anomaly Detection via Inverse Generative Adversary Network—0
A versatile anomaly detection method for medical images with a flow-based generative model in semi-supervision setting—0
An Intelligent and Time-Efficient DDoS Identification Framework for Real-Time Enterprise Networks SAD-F: Spark Based Anomaly Detection Framework—0
Regularized Cycle Consistent Generative Adversarial Network for Anomaly Detection—0
OIAD: One-for-all Image Anomaly Detection with Disentanglement Learning—0
Neighborhood Structure Assisted Non-negative Matrix Factorization and its Application in Unsupervised Point-wise Anomaly Detection—0
Simulation Assisted Likelihood-free Anomaly DetectionCode0
Unsupervised Learning of the Set of Local Maxima—0
Anomaly Detection with Density EstimationCode0
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
Semi-supervised Anomaly Detection using AutoEncodersCode1
Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging—0
Root Cause Detection Among Anomalous Time Series Using Temporal State AlignmentCode1
Characterizing Missing Information in Deep Networks Using Backpropagated Gradients—0
Integrative Tensor-based Anomaly Detection System For Satellites—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