Is data augmentation changing the train/test sets distribution?
I would argue that augmentation does not alter your train/test sets distribution.

I say this because I consider data augmentation to be part of your training pipeline and not part of your train set.

The pipeline below is one that I use often when someone asks about data augmentation. At the moment the data is split into train/test(/validation) sets, each set should have the same, or at least very similar distributions. This is the moment you need to worry about by picking a good splitting strategy.

Then, whatever you do with the train set, including data augmentation is part of your training pipeline. You will do data augmentation, like mentioned by others, do improve you model robustness. The validation and test set, do not need to be passed through your augmentation process.

          +-&gt; training set ---&gt; data augmentation --+
          |                                         |
          |                                         +-&gt; model training --+
          |                                         |                    |
all data -+-&gt; validation set -----------------------+                    |
          |                                                              +-&gt; model testing
          |                                                              |
          |                                                              |
          +-&gt; test set --------------------------------------------------+