When doing data augmentation in computer vision problems, should you train with the original (un-augmented) data as well or just the augmented data? Are there pros and cons to the two strategies or does it not matter?
In theory, if your augmentation is sensibly chosen and does not really change anything meaningful (e.g. rotation for satellite images), then it should not matter.
But there is certainly no harm in using the originals, too. Just make sure too don't use them so often that the model/ neutral network overfits them (perhaps use the exact originals just once, I'd speculate that you might want to use them in your very final iterations).