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 926–950 of 4856 papers

TitleStatusHype
U-Flow: A U-shaped Normalizing Flow for Anomaly Detection with Unsupervised ThresholdCode1
Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors—0
A geometric framework for outlier detection in high-dimensional data—0
A Generic Machine Learning Framework for Fully-Unsupervised Anomaly Detection with Contaminated Data—0
Anomaly Detection from a Tensor Train Perspective—0
Anomaly Detection Framework Using Rule Extraction for Efficient Intrusion Detection—0
Abnormal Object Recognition: A Comprehensive Study—0
Anomaly Detection for Water Treatment System based on Neural Network with Automatic Architecture Optimization—0
Anomaly Detection for Unmanned Aerial Vehicle Sensor Data Using a Stacked Recurrent Autoencoder Method with Dynamic Thresholding—0
A general-purpose method for applying Explainable AI for Anomaly Detection—0
Anomaly detection for the identification of volcanic unrest in satellite imagery—0
Deep Active Learning for Anomaly Detection—0
Adaptable and Interpretable Framework for Novelty Detection in Real-Time IoT Systems—0
SR-CIS: Self-Reflective Incremental System with Decoupled Memory and Reasoning—0
Anomaly Detection for Tabular Data with Internal Contrastive Learning—0
Anomaly Detection for Skin Disease Images Using Variational Autoencoder—0
Anomaly Detection for Scalable Task Grouping in Reinforcement Learning-based RAN Optimization—0
A General Framework for Unsupervised Anomaly Detection—0
ADALog: Adaptive Unsupervised Anomaly detection in Logs with Self-attention Masked Language Model—0
Anomaly Detection for Real-World Cyber-Physical Security using Quantum Hybrid Support Vector Machines—0
Anomaly Detection for People with Visual Impairments Using an Egocentric 360-Degree Camera—0
A general anomaly detection framework for fleet-based condition monitoring of machines—0
Anomaly Detection for Network Connection Logs—0
Anomaly Detection for Multivariate Time Series on Large-scale Fluid Handling Plant Using Two-stage Autoencoder—0
AGATE: Stealthy Black-box Watermarking for Multimodal Model Copyright Protection—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