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 3151–3200 of 4856 papers

TitleStatusHype
A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency—0
EPASAD: Ellipsoid decision boundary based Process-Aware Stealthy Attack Detector—0
Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems—0
Federated Learning for Distributed Spectrum Sensing in NextG Communication Networks—0
Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection—0
Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?—0
Distributed Anomaly Detection and Estimation over Sensor Networks: Observational-Equivalence and Q-Redundant Observer Design—0
A Survey on Graph Representation Learning Methods—0
SAM-kNN Regressor for Online Learning in Water Distribution NetworksCode0
Quadratic Neuron-empowered Heterogeneous Autoencoder for Unsupervised Anomaly DetectionCode0
IGRF-RFE: A Hybrid Feature Selection Method for MLP-based Network Intrusion Detection on UNSW-NB15 Dataset—0
syslrn: Learning What to Monitor for Efficient Anomaly DetectionCode0
Contextual Information Based Anomaly Detection for a Multi-Scene UAV Aerial VideosCode0
Radial Autoencoders for Enhanced Anomaly Detection—0
PAEDID: Patch Autoencoder Based Deep Image Decomposition For Pixel-level Defective Region Segmentation—0
Semi-supervised anomaly detection algorithm based on KL divergence (SAD-KL)—0
A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection—0
Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies—0
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos—0
From MIM-Based GAN to Anomaly Detection:Event Probability Influence on Generative Adversarial Networks—0
SIFT and SURF based feature extraction for the anomaly detectionCode0
Bayesian Nonparametric Submodular Video Partition for Robust Anomaly DetectionCode0
Domain-Generalized Textured Surface Anomaly Detection—0
Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked AutoencoderCode0
Diverse Counterfactual Explanations for Anomaly Detection in Time Series—0
Two methods for Jamming Identification in UAVs Networks using New Synthetic Dataset—0
FGAN: Federated Generative Adversarial Networks for Anomaly Detection in Network Traffic—0
ASE: Anomaly Scoring Based Ensemble Learning for Imbalanced Datasets—0
Subspace Modeling for Fast Out-Of-Distribution and Anomaly Detection—0
Learning from Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image AnalysisCode0
Machine Learning-based Anomaly Detection in Optical Fiber Monitoring—0
No Shifted Augmentations (NSA): compact distributions for robust self-supervised Anomaly Detection—0
The Analysis of Online Event Streams: Predicting the Next Activity for Anomaly DetectionCode0
Context-Dependent Anomaly Detection with Knowledge Graph Embedding Models—0
PiDAn: A Coherence Optimization Approach for Backdoor Attack Detection and Mitigation in Deep Neural Networks—0
Sensitivity Estimation for Dark Matter Subhalos in Synthetic Gaia DR2 using Deep Learning—0
A Framework for Verifiable and Auditable Federated Anomaly Detection—0
Driving Anomaly Detection Using Conditional Generative Adversarial Network—0
Practical data monitoring in the internet-services domainCode0
Feature space reduction as data preprocessing for the anomaly detectionCode0
LesionPaste: One-Shot Anomaly Detection for Medical Images—0
Multiple Inputs Neural Networks for Medicare fraud Detection—0
A Review of Open Source Software Tools for Time Series Analysis—0
TiSAT: Time Series Anomaly TransformerCode0
Machine Learning in NextG Networks via Generative Adversarial Networks—0
Anomaly Detection for Unmanned Aerial Vehicle Sensor Data Using a Stacked Recurrent Autoencoder Method with Dynamic Thresholding—0
Transfer Learning as an Essential Tool for Digital Twins in Renewable Energy Systems—0
Visual anomaly detection in video by variational autoencoder—0
Generative Cooperative Learning for Unsupervised Video Anomaly Detection—0
Battery Cloud with Advanced Algorithms—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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (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