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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 4451–4500 of 4856 papers

TitleStatusHype
EVAL: Explainable Video Anomaly Localization—0
Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study—0
Evaluating the Capabilities of Multi-modal Reasoning Models with Synthetic Task Data—0
Evaluating the Effectiveness of Video Anomaly Detection in the Wild: Online Learning and Inference for Real-world Deployment—0
Evaluation of a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery—0
Evaluation of an Anomaly Detector for Routers using Parameterizable Malware in an IoT Ecosystem—0
Evaluation of Color Anomaly Detection in Multispectral Images For Synthetic Aperture Sensing—0
Evaluation of Machine Learning-based Anomaly Detection Algorithms on an Industrial Modbus/TCP Data Set—0
Evaluation of Point Pattern Features for Anomaly Detection of Defect within Random Finite Set Framework—0
Evaluation Pipeline for systematically searching for Anomaly Detection Systems—0
Evaluation Strategy of Time-series Anomaly Detection with Decay Function—0
EvAn: Neuromorphic Event-based Anomaly Detection—0
EVA-S2PLoR: A Secure Element-wise Multiplication Meets Logistic Regression on Heterogeneous Database—0
Event2Graph: Event-driven Bipartite Graph for Multivariate Time-series Anomaly Detection—0
Event and Anomaly Detection Using Tucker3 Decomposition—0
Event-Based Dynamic Banking Network Exploration for Economic Anomaly Detection—0
Event Detection in Micro-PMU Data: A Generative Adversarial Network Scoring Method—0
EventVAD: Training-Free Event-Aware Video Anomaly Detection—0
Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategy—0
Experimental Assessment of Neural 3D Reconstruction for Small UAV-based Applications—0
Experiments on Anomaly Detection in Autonomous Driving by Forward-Backward Style Transfers—0
Expert enhanced dynamic time warping based anomaly detection—0
Explainable AI (XAI) for PHM of Industrial Asset: A State-of-The-Art, PRISMA-Compliant Systematic Review—0
Explainable Anomaly Detection: Counterfactual driven What-If Analysis—0
Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems—0
Explainable multi-class anomaly detection on functional data—0
Explainable Transformer-Based Anomaly Detection for Internet of Things Security Check for updates—0
Explainable Unsupervised Anomaly Detection with Random Forest—0
Explained anomaly detection in text reviews: Can subjective scenarios be correctly evaluated?—0
Explaining Anomalies with Tensor Networks—0
Explaining Deep Learning-based Anomaly Detection in Energy Consumption Data by Focusing on Contextually Relevant Data—0
Explanation Method for Anomaly Detection on Mixed Numerical and Categorical Spaces—0
Exploiting Autoencoder's Weakness to Generate Pseudo Anomalies—0
Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection—0
Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT—0
Exploiting Point-Language Models with Dual-Prompts for 3D Anomaly Detection—0
Exploiting Spatial-temporal Correlations for Video Anomaly Detection—0
Exploiting the Power of Levenberg-Marquardt Optimizer with Anomaly Detection in Time Series—0
Exploring Diffusion Models for Unsupervised Video Anomaly Detection—0
Exploring Dual Model Knowledge Distillation for Anomaly Detection—0
Exploring Global and Local Information for Anomaly Detection with Normal Samples—0
Exploring Human Crowd Patterns and Categorization in Video Footage for Enhanced Security and Surveillance using Computer Vision and Machine Learning—0
Exploring Information Centrality for Intrusion Detection in Large Networks—0
Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection—0
Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection—0
Exploring the Design of Adaptation Protocols for Improved Generalization and Machine Learning Safety—0
Exploring the impact of Optimised Hyperparameters on Bi-LSTM-based Contextual Anomaly Detector—0
Exploring the Impact of Outlier Variability on Anomaly Detection Evaluation Metrics—0
Exploring the Intrinsic Probability Distribution for Hyperspectral Anomaly Detection—0
Exploring the Magnitude-Shape Plot Framework for Anomaly Detection in Crowded Video Scenes—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