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 276300 of 4856 papers

TitleStatusHype
ADGym: Design Choices for Deep Anomaly DetectionCode1
An Unsupervised Short- and Long-Term Mask Representation for Multivariate Time Series Anomaly DetectionCode1
ADformer: A Multi-Granularity Transformer for EEG-Based Alzheimer's Disease AssessmentCode1
Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive LearningCode1
Deep Generative Classification of Blood Cell MorphologyCode1
Deep Orthogonal Hypersphere Compression for Anomaly DetectionCode1
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly DataCode1
AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly DetectionCode1
Anomaly Heterogeneity Learning for Open-set Supervised Anomaly DetectionCode1
Deep Contrastive One-Class Time Series Anomaly DetectionCode1
Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection in Machine Condition SoundsCode1
Deep Anomaly Detection Using Geometric TransformationsCode1
Anomaly Detection with Score Distribution DiscriminationCode1
Deep Anomaly Detection with Outlier ExposureCode1
AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language ModelsCode1
GLAD: Content-aware Dynamic Graphs For Log Anomaly DetectionCode1
Deep Anomaly Detection on Attributed NetworksCode1
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly DetectionCode1
Deep Dual Support Vector Data Description for Anomaly Detection on Attributed NetworksCode1
DATE: Detecting Anomalies in Text via Self-Supervision of TransformersCode1
Anomaly Detection in Medical Imaging with Deep Perceptual AutoencodersCode1
DAGAD: Data Augmentation for Graph Anomaly DetectionCode1
DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly DetectionCode1
DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security ApplicationsCode1
CutPaste: Self-Supervised Learning for Anomaly Detection and LocalizationCode1
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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