What brings the performance difference in Deep Learning with different data augmentation strategies? I am studying the performance of deep learning models toward abnormality detection in chest X-rays. 

Due to sparsity of data, I augment the data using different augmentation strategies including: 


Traditional augmentation methods (Gaussian smoothing, unsharp masking, and minimum filtering)
Generative Adversarial Networks


Contrary to the existing literature, I find that the models showed promising results with traditional augmentation methods (that i have mentioned herewith) than with GAN-generated synthetic images. 

What brings this performance difference?
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