  In this paper we propose the use of Generative Adversarial Networks (GAN) to
generate artificial training data for machine learning tasks. The generation of
artificial training data can be extremely useful in situations such as
imbalanced data sets, performing a role similar to SMOTE or ADASYN. It is also
useful when the data contains sensitive information, and it is desirable to
avoid using the original data set as much as possible (example: medical data).
We test our proposal on benchmark data sets using different network
architectures, and show that a Decision Tree (DT) classifier trained using the
training data generated by the GAN reached the same, (and surprisingly
sometimes better), accuracy and recall than a DT trained on the original data
set.
