  For machine learning task, lacking sufficient samples mean the trained model
has low confidence to approach the ground truth function. Until recently, after
the generative adversarial networks (GAN) had been proposed, we see the hope of
small samples data augmentation (DA) with realistic fake data, and many works
validated the viability of GAN-based DA. Although most of the works pointed out
higher accuracy can be achieved using GAN-based DA, some researchers stressed
that the fake data generated from GAN has inherent bias, and in this paper, we
explored when the bias is so low that it cannot hurt the performance, we set
experiments to depict the bias in different GAN-based DA setting, and from the
results, we design a pipeline to inspect specific dataset is
efficiently-augmentable with GAN-based DA or not. And finally, depending on our
trial to reduce the bias, we proposed some advice to mitigate bias in GAN-based
DA application.
