What brings the performance difference in Deep Learning with different data augmentation strategies?
In general, you have to be careful when using data augmentation. 

For example, doing rotation for this kind of image makes sense, we expect to see any of these images as potential 'real-life' example :




However, doing rotation for this kind of image is less meaningful. We don't expect to see this in 'real-life' example :




And GAN potentially makes generated image meaningless.
If your GAN produce 'thrash' augmented-data, then your network will train and learn 'thrash', which you don't want.

When you are training your model on GAN generated images, you're actually training your model to recognize GAN-generated image, not real-life example.

Sources:
 - towardsdatascience
 - quora question