With the development of manufacturing technology and the introduction of the smart factory, the productivity and the
amount of data to be acquired has significantly increased Recently, deep learning has been used to replace the existing
machine learning algorithms with improved predictive performances. With sufficient amount of data, solving
classification problems using the deep learning model can approximate any underlying probability. However, a typical
class imbalance in manufacturing field harms performance of the deep learning models. Although methods such as
resampling or reweighting data have been proposed to solve the data imbalance issue, such methods are not considered
as a fundamental solution to the issue. In this paper, we propose the Generative Adversarial Networks (GAN) which can
learn the distributions of a real data set, and generate a virtual dataset that will be utilized for numerous manufacturing
applications. To verify the proposed approach, we compared the performances of a model trained with the imbalanced
dataset and another model trained with data generated by GAN.