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 3676–3700 of 4856 papers

TitleStatusHype
Anomaly Detection Based on Selection and Weighting in Latent Space—0
ZYELL-NCTU NetTraffic-1.0: A Large-Scale Dataset for Real-World Network Anomaly Detection—0
Anomaly detection and automatic labeling for solar cell quality inspection based on Generative Adversarial Network—0
FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation—0
Event-Based Dynamic Banking Network Exploration for Economic Anomaly Detection—0
Neural Network-based Quantization for Network Automation—0
Helicopter Track Identification with Autoencoder—0
Image/Video Deep Anomaly Detection: A Survey—0
AdeNet: Deep learning architecture that identifies damaged electrical insulators in power linesCode0
Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection—0
Online anomaly detection using statistical leverage for streaming business process eventsCode0
Automatic Feature Extraction for Heartbeat Anomaly DetectionCode0
Railway Anomaly detection model using synthetic defect images generated by CycleGAN—0
Deep Compact Polyhedral Conic Classifier for Open and Closed Set Recognition—0
Sleep Apnea and Respiratory Anomaly Detection from a Wearable Band and Oxygen Saturation—0
An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery—0
Robust and Transferable Anomaly Detection in Log Data using Pre-Trained Language Models—0
Unsupervised Brain Anomaly Detection and Segmentation with Transformers—0
Neuroscience-Inspired Algorithms for the Predictive Maintenance of Manufacturing Systems—0
Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-raysCode0
Unsupervised Clustering of Time Series Signals using Neuromorphic Energy-Efficient Temporal Neural Networks—0
Interpretable Stability Bounds for Spectral Graph Filters—0
Topological Obstructions to Autoencoding—0
Anomaly Detection for Scenario-based Insider Activities using CGAN Augmented Data—0
Towards AIOps in Edge Computing Environments—0
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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