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 4176–4200 of 4856 papers

TitleStatusHype
Deep Learning, Machine Learning, Advancing Big Data Analytics and Management—0
Deep learning models for predictive maintenance: a survey, comparison, challenges and prospect—0
Deep Learning Models for Visual Inspection on Automotive Assembling Line—0
Deep Learning on Operational Facility Data Related to Large-Scale Distributed Area Scientific Workflows—0
Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review—0
Deep Multi-Task Learning for Anomalous Driving Detection Using CAN Bus Scalar Sensor Data—0
Deep Nearest Neighbor Anomaly Detection—0
Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks—0
Deep Probabilistic Modeling of User Behavior for Anomaly Detection via Mixture Density Networks—0
DeepQC: A Deep Learning System for Automatic Quality Control of In-situ Soil Moisture Sensor Time Series Data—0
Deep RAN: A Scalable Data-driven platform to Detect Anomalies in Live Cellular Network Using Recurrent Convolutional Neural Network—0
Deep Random Projection Outlyingness for Unsupervised Anomaly Detection—0
Deep-RBF Networks for Anomaly Detection in Automotive Cyber-Physical Systems—0
Deep Representation Learning for Social Network Analysis—0
Deep Semi-Supervised Anomaly Detection for Finding Fraud in the Futures Market—0
Deep Spatio-temporal Sparse Decomposition for Trend Prediction and Anomaly Detection in Cardiac Electrical Conduction—0
Deep Structured Cross-Modal Anomaly Detection—0
DeepTimeAnomalyViz: A Tool for Visualizing and Post-processing Deep Learning Anomaly Detection Results for Industrial Time-Series—0
Deep unsupervised anomaly detection—0
Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework—0
Deep Variational Semi-Supervised Novelty Detection—0
Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models—0
Deep Video Anomaly Detection: Opportunities and Challenges—0
Deep Visual Anomaly detection with Negative Learning—0
Defect Detection Approaches Based on Simulated Reference Image—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