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 24762500 of 4856 papers

TitleStatusHype
Data-Driven Thermal Anomaly Detection in Large Battery Packs0
Masked Transformer for image Anomaly Localization0
A Hierarchical Approach to Conditional Random Fields for System Anomaly Detection0
AltUB: Alternating Training Method to Update Base Distribution of Normalizing Flow for Anomaly Detection0
Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detectionCode1
AD-DMKDE: Anomaly Detection through Density Matrices and Fourier FeaturesCode0
Detecting fake accounts through Generative Adversarial Network in online social media0
Deep Crowd Anomaly Detection: State-of-the-Art, Challenges, and Future Research Directions0
InForecaster: Forecasting Influenza Hemagglutinin Mutations Through the Lens of Anomaly DetectionCode0
Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical EncodingsCode1
Bridging Machine Learning and Sciences: Opportunities and Challenges0
Self-supervised Sparse Representation for Video Anomaly DetectionCode1
Multi-Scale Patch-Based Representation Learning for Image Anomaly Detection and Segmentation0
Improving the Anomaly Detection in GPR Images by Fine-Tuning CNNs with Synthetic Data0
Frequency of Interest-based Noise Attenuation Method to Improve Anomaly Detection Performance0
Hierarchical Deep Learning with Generative Adversarial Network for Automatic Cardiac Diagnosis from ECG Signals0
Time and Cost-Efficient Bathymetric Mapping System using Sparse Point Cloud Generation and Automatic Object DetectionCode0
ADPS: Asymmetric Distillation Post-Segmentation for Image Anomaly Detection0
Anomaly Detection Requires Better RepresentationsCode1
Estimating the Contamination Factor's Distribution in Unsupervised Anomaly DetectionCode1
Graph Anomaly Detection with Unsupervised GNNsCode1
DAGAD: Data Augmentation for Graph Anomaly DetectionCode1
TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency AnalysisCode1
Spatio-Temporal-based Context Fusion for Video Anomaly Detection0
Towards Generating Adversarial Examples on Mixed-type Data0
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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