  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.
