I am thinking a simple question just came in my mind.
There are many people use data augmentation on their image data to train deep CNN.
When I learn from Andrew Ng's DL courses, he mentioned that to train a better model, you generally need to have your train/test data come from the same distribution. Like, if you train you CNN with a lot of images of cars and human, it's (usually) not a good idea to use it to classify a cat or a dog.
After the augmentation, my training data is kind of boosted, then, can I still say my train/test data set come from a same distribution? Am I violating what he said? Why? Or why not?