I'm training a deep learning model to map binary images to grayscale values of the same shape. For the dataset, I can genearate one as large and diverse as I want it to be.
My question is: let's say the original dataset I created contains 100k images. I can either generate another 900k unique images (so that my training set is 1M in total), or to use data augemntation on the ones I already have and somehow (flipping, rotating, etc.) generate another 900k (I know that in 2D, there's probably only 8 different types of unique images that can be generated by flipping/rotating or a combination, but that's beside the point here).
Which one would you go for and why? Thank you so much!