Does it make sense to train a WGAN/ or any GAN network for that matter on examples as few as 100? In order to augment the data set, catering to data imbalance. Does it make sense or will I just be creating the same examples again? (I ran a WGAN with GP for 5000 iterations) Will it overfit the data?