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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 1751–1800 of 4856 papers

TitleStatusHype
MFL Data Preprocessing and CNN-based Oil Pipeline Defects Detection—0
Anomaly Detection in Power Generation Plants with Generative Adversarial Networks—0
Unravel Anomalies: An End-to-end Seasonal-Trend Decomposition Approach for Time Series Anomaly DetectionCode1
Probabilistic Sampling-Enhanced Temporal-Spatial GCN: A Scalable Framework for Transaction Anomaly Detection in Ethereum Networks—0
Algorithmic Recourse for Anomaly Detection in Multivariate Time Series—0
Weakly-Supervised Video Anomaly Detection with Snippet Anomalous AttentionCode0
ADGym: Design Choices for Deep Anomaly DetectionCode1
Digital Twin-based Anomaly Detection with Curriculum Learning in Cyber-physical SystemsCode0
Human Kinematics-inspired Skeleton-based Video Anomaly DetectionCode0
SeMAnD: Self-Supervised Anomaly Detection in Multimodal Geospatial Datasets—0
Divide and Conquer in Video Anomaly Detection: A Comprehensive Review and New ApproachCode0
LogGPT: Log Anomaly Detection via GPTCode1
Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernelsCode1
Sampling - Variational Auto Encoder - Ensemble: In the Quest of Explainable Artificial Intelligence—0
Towards Surveillance Video-and-Language Understanding: New Dataset, Baselines, and Challenges—0
(Predictable) Performance Bias in Unsupervised Anomaly Detection—0
Real3D-AD: A Dataset of Point Cloud Anomaly DetectionCode1
NetDiffus: Network Traffic Generation by Diffusion Models through Time-Series ImagingCode1
STemGAN: spatio-temporal generative adversarial network for video anomaly detection—0
On Data Fabrication in Collaborative Vehicular Perception: Attacks and CountermeasuresCode1
mixed attention auto encoder for multi-class industrial anomaly detection—0
Generative Pre-Training of Time-Series Data for Unsupervised Fault Detection in Semiconductor Manufacturing—0
Ano-SuPs: Multi-size anomaly detection for manufactured products by identifying suspected patches—0
Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward—0
Two-stage coarse-to-fine image anomaly segmentation and detection modelCode1
Semi-automatic staging area for high-quality structured data extraction from scientific literatureCode0
LiON: Learning Point-wise Abstaining Penalty for LiDAR Outlier DetectioN Using Diverse Synthetic DataCode1
Autoencoder-based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter—0
Active anomaly detection based on deep one-class classification—0
An Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contamination—0
Forensic Video Analytic Software—0
Context-Aware Deep Time-Series Decomposition for Anomaly Detection in BusinessesCode0
Enhancing Visual Perception in Novel Environments via Incremental Data Augmentation Based on Style TransferCode0
Understanding the limitations of self-supervised learning for tabular anomaly detection—0
TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection—0
An Automated Machine Learning Approach for Detecting Anomalous Peak Patterns in Time Series Data from a Research Watershed in the Northeastern United States Critical Zone—0
Beta quantile regression for robust estimation of uncertainty in the presence of outliers—0
FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly DetectionCode1
Autoencoder-Based Visual Anomaly Localization for Manufacturing Quality Control—0
Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive LearningCode1
GLAD: Content-aware Dynamic Graphs For Log Anomaly DetectionCode1
Quantized Non-Volatile Nanomagnetic Synapse based Autoencoder for Efficient Unsupervised Network Anomaly Detection—0
Introducing Shape Prior Module in Diffusion Model for Medical Image Segmentation—0
Effective Abnormal Activity Detection on Multivariate Time Series Healthcare Data—0
DAD++: Improved Data-free Test Time Adversarial DefenseCode0
Knowledge Distillation-Empowered Digital Twin for Anomaly Detection—0
Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data—0
TSGBench: Time Series Generation BenchmarkCode1
A Critical Review of Common Log Data Sets Used for Evaluation of Sequence-based Anomaly Detection TechniquesCode1
Reasonable Anomaly Detection in Long SequencesCode0
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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