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 2101–2150 of 4856 papers

TitleStatusHype
Anomaly Detection via Multi-Scale Contrasted Memory—0
Convolutional Neural Network Design and Evaluation for Real-Time Multivariate Time Series Fault Detection in Spacecraft Attitude Sensors—0
Convolutional Graph Auto-encoder: A Deep Generative Neural Architecture for Probabilistic Spatio-temporal Solar Irradiance Forecasting—0
A deep learning-based approach for the automated surface inspection of copper clad laminate images—0
A2B-GAN: Utilizing Unannotated Anomalous Images for Anomaly Detection in Medical Image Analysis—0
Deep Generative Design: Integration of Topology Optimization and Generative Models—0
Genetic Neural Architecture Search for automatic assessment of human sperm images—0
Convolutional Ensembling based Few-Shot Defect Detection Technique—0
Convolutional Autoencoders for Data Compression and Anomaly Detection in Small Satellite Technologies—0
Anomaly Detection via Minimum Likelihood Generative Adversarial Networks—0
Control theoretically explainable application of autoencoder methods to fault detection in nonlinear dynamic systems—0
Controllable RANSAC-based Anomaly Detection via Hypothesis Testing—0
Anomaly Detection via Learning-Based Sequential Controlled Sensing—0
A Deep Learning Approach to Video Anomaly Detection using Convolutional Autoencoders—0
Contrastive Structured Anomaly Detection for Gaussian Graphical Models—0
Anomaly Detection via Gumbel Noise Score Matching—0
Contrastive-Regularized U-Net for Video Anomaly Detection—0
A Multi-Level Approach for Class Imbalance Problem in Federated Learning for Remote Industry 4.0 Applications—0
A Combination of Temporal Sequence Learning and Data Description for Anomaly-based NIDS—0
Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Data—0
Contrastive Predictive Coding for Anomaly Detection—0
Anomaly Detection via Federated Learning—0
Contrastive Learning for Time Series on Dynamic Graphs—0
Anomaly Detection via Controlled Sensing and Deep Active Inference—0
A multi-domain splitting framework for time-varying graph structure—0
Generative AI Enabled Robust Sensor Placement in Cyber-Physical Power Systems: A Graph Diffusion Approach—0
Anomaly Detection via Autoencoder Composite Features and NCE—0
Learning Informative Health Indicators Through Unsupervised Contrastive Learning—0
A Deep Learning Approach for Ontology Enrichment from Unstructured Text—0
Anomaly Detection Utilizing a Riemann Metric for Robust Myoelectric Pattern Recognition—0
Continuous Test-time Domain Adaptation for Efficient Fault Detection under Evolving Operating Conditions—0
Grading and Anomaly Detection for Automated Retinal Image Analysis using Deep Learning—0
Generative Anomaly Detection for Time Series Datasets—0
Anomaly Detection Using the Knowledge-based Temporal Abstraction Method—0
Continuous GNN-based Anomaly Detection on Edge using Efficient Adaptive Knowledge Graph Learning—0
A Modular and Unified Framework for Detecting and Localizing Video Anomalies—0
Continual learning on deployment pipelines for Machine Learning Systems—0
Generation of Granular-Balls for Clustering Based on the Principle of Justifiable Granularity—0
Continual Learning for Anomaly Detection in Surveillance Videos—0
A Deep Learning Anomaly Detection Method in Textual Data—0
Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection—0
A Modified Dynamic Time Warping (MDTW) Approach and Innovative Average Non-Self Match Distance (ANSD) Method for Anomaly Detection in ECG Recordings—0
Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing—0
Generating Synthetic X-ray Images of a Person from the Surface Geometry—0
Generation of Anonymous Chest Radiographs Using Latent Diffusion Models for Training Thoracic Abnormality Classification Systems—0
Generative Adversarial Evasion and Out-of-Distribution Detection for UAV Cyber-Attacks—0
Contextual Affinity Distillation for Image Anomaly Detection—0
Anomaly Detection Using One-Class SVM for Logs of Juniper Router Devices—0
Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection—0
A Novel Spatiotemporal Correlation Anomaly Detection Method Based on Time-Frequency-Domain Feature Fusion and a Dynamic Graph Neural Network in Wireless Sensor Network—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