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 1426–1450 of 4856 papers

TitleStatusHype
A Novel Anomaly Detection Method for Multimodal WSN Data Flow via a Dynamic Graph Neural Network—0
An Automated Analysis Framework for Trajectory Datasets—0
A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised—0
A digital eye-fixation biomarker using a deep anomaly scheme to classify Parkisonian patterns—0
Detection and Statistical Modeling of Birth-Death Anomaly—0
D\'etection automatique d'anomalies sur deux styles de parole dysarthrique: parole lue vs spontan\'ee (Automatic anomaly detection for dysarthria across two speech styles : read vs spontaneous speech)—0
Ano-SuPs: Multi-size anomaly detection for manufactured products by identifying suspected patches—0
An Attribute Oriented Induction based Methodology for Data Driven Predictive Maintenance—0
Power-Grid Controller Anomaly Detection with Enhanced Temporal Deep Learning—0
An Origami-Inspired Endoscopic Capsule with Tactile Perception for Early Tissue Anomaly Detection—0
Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes—0
A Dictionary Approach to EBSD Indexing—0
AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by Random Labeling—0
AAD-LLM: Adaptive Anomaly Detection Using Large Language Models—0
Detection and Analysis of Drive-by-Download Attacks and Malicious JavaScript Code—0
Détection d’anomalies textuelles à base de l’ingénierie d’invite (Prompt Engineering-Based Text Anomaly Detection )—0
Detection of Anomalies in Multivariate Time Series Using Ensemble Techniques—0
An optimization method for out-of-distribution anomaly detection models—0
AnoSeg: Anomaly Segmentation Network Using Self-Supervised Learning—0
Deep Autoencoders for Unsupervised Anomaly Detection in Wildfire Prediction—0
Deep Autoencoders with Value-at-Risk Thresholding for Unsupervised Anomaly Detection—0
An Attention Free Conditional Autoencoder For Anomaly Detection in Cryptocurrencies—0
Deep Autoencoding GMM-based Unsupervised Anomaly Detection in Acoustic Signals and its Hyper-parameter Optimization—0
A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata—0
Deep Anomaly Detection on Tennessee Eastman Process Data—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