5 citations · 5 across the 3 of their papers we have counts for
3 papers · 2 filters
Robust Anomaly Detection in Industrial Environments via Meta-Learning
Muhammad Aqeel, Shakiba Sharifi, Marco Cristani +1
Anomaly detection is fundamental for ensuring quality control and operational efficiency in industrial environments, yet conventional approaches face significant challenges when tr…
Towards Real Unsupervised Anomaly Detection Via Confident Meta-Learning
Muhammad Aqeel, Shakiba Sharifi, Marco Cristani +1
So-called unsupervised anomaly detection is better described as semi-supervised, as it assumes all training data are nominal. This assumption simplifies training but requires manua…
Meta Learning-Driven Iterative Refinement for Robust Anomaly Detection in Industrial Inspection
Muhammad Aqeel, Shakiba Sharifi, Marco Cristani +1
This study investigates the performance of robust anomaly detection models in industrial inspection, focusing particularly on their ability to handle noisy data. We propose to leve…