text: This paper presents an empirical study of whether data augmentation can be a substitute for explicit regularization of weight decay and dropout. It is a well written and well organized paper. However, overall I do not find the authors’ premises and conclusions to be well supported by the results and would suggest further investigations. In particular: a) Data augmentation is a very domain specific process and limits of augmentation are often not clear. For example, in financial data or medical imaging data it is often not clear how data augmentation should be carried out and how much is too much. On the other hand model regularization is domain agnostic (has to be tuned for each task, but the methodology is consistent and well known). Thus advocating that data augmentation can universally replace explicit regularization does not seem correct. b) I find the results to be somewhat inconsistent. For example, on CIFAR-10, for 100% data regularization+augmentation is better than augmentation alone for both models, whereas for 80% data augmentation alone seems to be better. Similarly on CIFAR-100 the WRN model shows mixed trends, and this model is significantly better than the All-CNN model in performance. These results also seem inconsistent with authors statement “…and conclude that data augmentation alone - without any other explicit regularization techniques - can achieve the same performance to higher as regularized models…”