I am thinking a simple question which just came to my mind.

There are many people using 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 comes from the same distribution? Am I violating what he said? Why? Or, why not?