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

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Showing 3701–3725 of 4856 papers

TitleStatusHype
Collective Awareness for Abnormality Detection in Connected Autonomous Vehicles—0
Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour modelsCode1
Enhanced Cyber-Physical Security Using Attack-resistant Cyber Nodes and Event-triggered Moving Target Defence—0
Smart Anomaly Detection in Sensor Systems: A Multi-Perspective Review—0
Model Extraction Attacks on Graph Neural Networks: Taxonomy and RealizationCode0
Low-rank on Graphs plus Temporally Smooth Sparse Decomposition for Anomaly Detection in Spatiotemporal Data—0
Network Anomaly Detection Using Federated Learning and Transfer Learning—0
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health EpisodesCode0
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity MicrocontrollersCode1
Anomaly Detection for Multivariate Time Series of Exotic Supernovae—0
Automating Abnormality Detection in Musculoskeletal Radiographs through Deep Learning—0
Anomaly Detection in a Large-scale Cloud Platform—0
An Empirical Investigation of Contextualized Number PredictionCode0
Graph Fairing Convolutional Networks for Anomaly DetectionCode0
A Federated Learning Approach to Anomaly Detection in Smart Buildings—0
Action Sequence Augmentation for Early Graph-based Anomaly DetectionCode0
Anomaly Detection on X-Rays Using Self-Supervised Aggregation Learning—0
Addressing Variance Shrinkage in Variational Autoencoders using Quantile Regression—0
Reconstruction by Inpainting for Visual Anomaly DetectionCode1
On the Usage of Generative Models for Network Anomaly Detection in Multivariate Time-Series—0
Unsupervised Video Anomaly Detection via Normalizing Flows with Implicit Latent Features—0
Reconstruct Anomaly to Normal: Adversarial Learned and Latent Vector-constrained Autoencoder for Time-series Anomaly Detection—0
Detecting Anomalies from Video-Sequences: a Novel Descriptor—0
Anomaly Detection With Conditional Variational Autoencoders—0
PANDA: Adapting Pretrained Features for Anomaly Detection and SegmentationCode1
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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