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Deep learning (DL) techniques are highly effective for defect detection fromimages. Training DL classification models, however, requires vast amounts oflabeled data which is often expensive to collect. In many cases, not only theavailable training data is limited but may also imbalanced. In this paper, wepropose a novel domain adaptation (DA) approach to address the problem oflabeled training data scarcity for a target learning task by transferringknowledge gained from an existing source dataset used for a similar learningtask. Our approach works for scenarios where the source dataset and the datasetavailable for the target learning task have same or different feature spaces.We combine our DA approach with an autoencoder-based data augmentation approachto address the problem of imbalanced target datasets. We evaluate our combinedapproach using image data for wafer defect prediction. The experiments show itssuperior performance against other algorithms when the number of labeledsamples in the target dataset is significantly small and the target dataset isimbalanced.

Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation