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

Showing 17761800 of 4856 papers

TitleStatusHype
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 Calorimeter0
Active anomaly detection based on deep one-class classification0
An Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contamination0
Forensic Video Analytic Software0
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 detection0
TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection0
An Automated Machine Learning Approach for Detecting Anomalous Peak Patterns in Time Series Data from a Research Watershed in the Northeastern United States Critical Zone0
Beta quantile regression for robust estimation of uncertainty in the presence of outliers0
FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly DetectionCode1
Autoencoder-Based Visual Anomaly Localization for Manufacturing Quality Control0
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 Detection0
Introducing Shape Prior Module in Diffusion Model for Medical Image Segmentation0
Effective Abnormal Activity Detection on Multivariate Time Series Healthcare Data0
DAD++: Improved Data-free Test Time Adversarial DefenseCode0
Knowledge Distillation-Empowered Digital Twin for Anomaly Detection0
Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data0
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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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016Unverified
2CPR-fast(TensorRT)FPS362Unverified
3CPR(TensorRT)FPS130Unverified
4GLASSDetection AUROC99.9Unverified
5UniNetDetection AUROC99.9Unverified
6HETMMDetection AUROC99.8Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9DDADDetection AUROC99.8Unverified
10PBASDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
5DDADDetection AUROC98.9Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
7DiffusionADDetection AUROC98.8Unverified
8GLASSDetection AUROC98.8Unverified
9TransFusionDetection AUROC98.7Unverified
10HETMMDetection AUROC98.1Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3Unverified
2PSADAvg. Detection AUROC94.9Unverified