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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 4401–4450 of 4856 papers

TitleStatusHype
Rule Extraction in Unsupervised Anomaly Detection for Model Explainability: Application to OneClass SVMCode0
UN-AVOIDS: Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant Detection ScoringCode0
Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHCCode0
Anomaly Detection using Principles of Human PerceptionCode0
BiPOCO: Bi-Directional Trajectory Prediction with Pose Constraints for Pedestrian Anomaly DetectionCode0
Graph Laplacian for Image Anomaly DetectionCode0
Graph Fairing Convolutional Networks for Anomaly DetectionCode0
An anomaly detection approach for backdoored neural networks: face recognition as a case studyCode0
SADDE: Semi-supervised Anomaly Detection with Dependable ExplanationsCode0
Multivariate Time Series Anomaly Detection: Fancy Algorithms and Flawed Evaluation MethodologyCode0
Graph Embedded Pose Clustering for Anomaly DetectionCode0
Multivariate Time Series Anomaly Detection using DiffGAN ModelCode0
Label-based Graph Augmentation with Metapath for Graph Anomaly DetectionCode0
TeVAE: A Variational Autoencoder Approach for Discrete Online Anomaly Detection in Variable-state Multivariate Time-series DataCode0
BINet: Multi-perspective Business Process Anomaly ClassificationCode0
SAIFE: Unsupervised Wireless Spectrum Anomaly Detection with Interpretable FeaturesCode0
Multi-view Deep One-class Classification: A Systematic ExplorationCode0
ADKGD: Anomaly Detection in Knowledge Graphs with Dual-Channel TrainingCode0
VegaEdge: Edge AI Confluence Anomaly Detection for Real-Time Highway IoT-ApplicationsCode0
Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly DetectionCode0
GradStop: Exploring Training Dynamics in Unsupervised Outlier Detection through GradientCode0
Good Practices and A Strong Baseline for Traffic Anomaly DetectionCode0
Mutually exciting point process graphs for modelling dynamic networksCode0
GLADMamba: Unsupervised Graph-Level Anomaly Detection Powered by Selective State Space ModelCode0
GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario DetectionCode0
WAIC, but Why? Generative Ensembles for Robust Anomaly DetectionCode0
SaLite : A light-weight model for salient object detectionCode0
Deep Semi-Supervised Anomaly DetectionCode0
Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time SeriesCode0
N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep AutoencodersCode0
SAM-kNN Regressor for Online Learning in Water Distribution NetworksCode0
Generator Based Inference (GBI)Code0
Uncertainty on Asynchronous Time Event PredictionCode0
Generative Optimization Networks for Memory Efficient Data GenerationCode0
NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic NetworksCode0
Uncertainty Quantification in Anomaly Detection with Cross-Conformal p-ValuesCode0
BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly DetectionCode0
Anomaly detection using prediction error with Spatio-Temporal Convolutional LSTMCode0
Unsupervised Learning of Anomaly Detection from Contaminated Image Data using Simultaneous Encoder TrainingCode0
BiGSeT: Binary Mask-Guided Separation Training for DNN-based Hyperspectral Anomaly DetectionCode0
Generative Neural Networks for Anomaly Detection in Crowded ScenesCode0
The Analysis of Online Event Streams: Predicting the Next Activity for Anomaly DetectionCode0
Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier featuresCode0
Network Traffic Anomaly Detection Using Recurrent Neural NetworksCode0
The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth MeasureCode0
Scalable Motif Counting for Large-scale Temporal GraphsCode0
AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing PipelinesCode0
Neural Collaborative Filtering to Detect Anomalies in Human Semantic TrajectoriesCode0
General Domain Adaptation Through Proportional Progressive Pseudo LabelingCode0
The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron ColliderCode0
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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