Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation
This addresses the challenge of data scarcity and imbalance in manufacturing defect detection, offering a practical solution for industries with limited labeled data, though it is incremental as it builds on existing domain adaptation and augmentation techniques.
The paper tackles the problem of training deep learning models for defect detection with limited and imbalanced labeled data by proposing a domain adaptation approach combined with data augmentation. The result shows superior performance in wafer defect prediction, especially with small and imbalanced target datasets, as demonstrated in experiments against other algorithms.
Deep learning (DL) techniques are highly effective for defect detection from images. Training DL classification models, however, requires vast amounts of labeled data which is often expensive to collect. In many cases, not only the available training data is limited but may also imbalanced. In this paper, we propose a novel domain adaptation (DA) approach to address the problem of labeled training data scarcity for a target learning task by transferring knowledge gained from an existing source dataset used for a similar learning task. Our approach works for scenarios where the source dataset and the dataset available for the target learning task have same or different feature spaces. We combine our DA approach with an autoencoder-based data augmentation approach to address the problem of imbalanced target datasets. We evaluate our combined approach using image data for wafer defect prediction. The experiments show its superior performance against other algorithms when the number of labeled samples in the target dataset is significantly small and the target dataset is imbalanced.