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 4101–4125 of 4856 papers

TitleStatusHype
Anomaly Detection in Particulate Matter Sensor using Hypothesis Pruning Generative Adversarial Network—0
An Attribute Oriented Induction based Methodology for Data Driven Predictive Maintenance—0
Semi-Supervised Learning of Bearing Anomaly Detection via Deep Variational Autoencoders—0
XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation LearningCode0
ACE -- An Anomaly Contribution Explainer for Cyber-Security Applications—0
Learning Representations for Time Series ClusteringCode0
Transfer Anomaly Detection by Inferring Latent Domain Representations—0
A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients—0
Sparse-GAN: Sparsity-constrained Generative Adversarial Network for Anomaly Detection in Retinal OCT Image—0
Free-riders in Federated Learning: Attacks and Defenses—0
High- and Low-level image component decomposition using VAEs for improved reconstruction and anomaly detection—0
Host-based anomaly detection using Eigentraces feature extraction and one-class classification on system call trace data—0
Attribute Restoration Framework for Anomaly DetectionCode0
AnoNet: Weakly Supervised Anomaly Detection in Textured Surfaces—0
Latent space conditioning for improved classification and anomaly detection—0
EvAn: Neuromorphic Event-based Anomaly Detection—0
Rule Extraction in Unsupervised Anomaly Detection for Model Explainability: Application to OneClass SVMCode0
Generalizing Information to the Evolution of Rational Belief—0
Log Message Anomaly Detection and Classification Using Auto-B/LSTM and Auto-GRU—0
A Framework for End-to-End Deep Learning-Based Anomaly Detection in Transportation Networks—0
A Promotion Method for Generation Error Based Video Anomaly Detection—0
On the Impact of Object and Sub-component Level Segmentation Strategies for Supervised Anomaly Detection within X-ray Security Imagery—0
Attention Guided Anomaly Localization in Images—0
Seq2Seq RNN based Gait Anomaly Detection from Smartphone Acquired Multimodal Motion DataCode0
Deep Anomaly Detection with Deviation NetworksCode0
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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