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 3701–3750 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
Multi-Source Anomaly Detection in Distributed IT Systems—0
Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly GeneratorsCode0
Anomaly Detection for Aggregated Data Using Multi-Graph Autoencoder—0
NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection—0
Detecting Log Anomalies with Multi-Head Attention (LAMA)—0
Adaptive Immunity for Software: Towards Autonomous Self-healing Systems—0
Copula Quadrant Similarity for Anomaly Scores—0
Ensemble and Random Collaborative Representation-Based Anomaly Detector for Hyperspectral Imagery—0
Industrial Image Anomaly Localization Based on Gaussian Clustering of Pretrained FeatureCode0
Label Augmentation via Time-based Knowledge Distillation for Financial Anomaly Detection—0
Deep unsupervised anomaly detection—0
A Survey on Embedding Dynamic Graphs—0
Anomaly Recognition from surveillance videos using 3D Convolutional Neural Networks—0
Smart Black Box 2.0: Efficient High-bandwidth Driving Data Collection based on Video Anomalies—0
Regularization-based Continual Learning for Anomaly Detection in Discrete Manufacturing—0
Dance With Self-Attention: A New Look of Conditional Random Fields on Anomaly Detection in Videos—0
MCM-aware Twin-least-square GAN for Hyperspectral Anomaly Detection—0
Learning Unsupervised Metaformer for Anomaly Detection—0
Learning Deep Latent Variable Models via Amortized Langevin Dynamics—0
Iterative Image Inpainting with Structural Similarity Mask for Anomaly Detection—0
GenAD: General Representations of Multivariate Time Series for Anomaly Detection—0
Domain Adaptation via Anaomaly Detection—0
Data Transformer for Anomalous Trajectory Detection—0
Sparse Coding-inspired GAN for Weakly Supervised Hyperspectral Anomaly Detection—0
Anomaly detection in dynamical systems from measured time series—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