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 3701–3725 of 4856 papers

TitleStatusHype
How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?—0
Mutually exciting point process graphs for modelling dynamic networksCode0
LIME: Low-Cost and Incremental Learning for Dynamic Heterogeneous Information NetworksCode0
Anomaly Detection through Transfer Learning in Agriculture and Manufacturing IoT Systems—0
On the Properties of Kullback-Leibler Divergence Between Multivariate Gaussian Distributions—0
VeeAlign: Multifaceted Context Representation using Dual Attention for Ontology AlignmentCode0
Exact Optimization of Conformal Predictors via Incremental and Decremental LearningCode0
Graph Coding for Model Selection and Anomaly Detection in Gaussian Graphical Models—0
SAFELearning: Enable Backdoor Detectability In Federated Learning With Secure Aggregation—0
Evaluation of Point Pattern Features for Anomaly Detection of Defect within Random Finite Set Framework—0
Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder—0
AURSAD: Universal Robot Screwdriving Anomaly Detection DatasetCode0
Decentralized Federated Learning Preserves Model and Data Privacy—0
Robust Attack Detection Approach for IIoT Using Ensemble Classifier—0
The Deep Radial Basis Function Data Descriptor (D-RBFDD) Network: A One-Class Neural Network for Anomaly Detection—0
Autoencoder-based Condition Monitoring and Anomaly Detection Method for Rotating Machines—0
Wisdom of the Contexts: Active Ensemble Learning for Contextual Anomaly Detection—0
Blind Image Denoising and Inpainting Using Robust Hadamard AutoencodersCode0
Leveraging 3D Information in Unsupervised Brain MRI Segmentation—0
Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey—0
Variational Autoencoders with a Structural Similarity Loss in Time of Flight MRAs—0
Flow Forecast: A deep learning for time series forecasting, classification, and anomaly detection framework built in PyTorch—0
A Non-Parametric Subspace Analysis Approach with Application to Anomaly Detection Ensembles—0
Anomaly Detection Support Using Process Classification—0
Deep State Inference: Toward Behavioral Model Inference of Black-box Software SystemsCode0
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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