Modern deep artificial neural networks have achieved impressive results through models with orders of magnitude more parameters than training examples which control overfitting with the help of regularization. Regularization can be implicit, as is the case of stochastic gradient descent and parameter sharing in convolutional layers, or explicit. Explicit regularization techniques, most common forms are weight decay and dropout, have proven successful in terms of improved generalization, but they introduce sensitive hyper-parameters and, incongruously, often require deeper and wider architectures to compensate for the reduced capacity. In contrast, data augmentation techniques exploit domain knowledge to increase the number of training examples and improve generalization without reducing the representational capacity and without introducing model-dependent parameters, since it is applied on the training data. In this paper we systematically contrast data augmentation and explicit regularization on three popular architectures and three image object classification data sets. Our results demonstrate that data augmentation alone can achieve the same performance or higher as regularized models and exhibits much higher adaptability to changes in the architecture and the amount of training data.
This paper proposed to adopt data augmentation over explicit regularization (weight decay and dropout), supported by a comparative study of 3 ConvNets (all-conv, wide-resnet and densenet) on three image classification benchmarks (ImageNet, CIFAR-10 and CIFAR-100), as well as variants of experiments such as training with smaller training set.
Most of the observations presented here are largely known to the community, though I think at least a few of them are worth emphasizing:
1. One major downside of explicit regularization the requirement to tune hyper-parameters on each different scenarios. For example, the paper shows that the default hyper-parameter becomes suboptimal when the architecture becomes either deeper or shallower.
2. Given strong enough data augmentation, sometimes turning off weight decay / dropout could give better test performance (i.e. the optimal hyper-parameters are zeros in those cases).
That being said, I think this the paper at the current state does not contain sufficient message to stand as a full paper. I list a few areas that I think could potentially improve the paper if properly addressed:
1. Proper definition of explicit / implicit regularization: one goal this paper is trying to achieve is an unambiguous definition of explicit / implicit regularization. However, the current definition given by the paper is not any less ambiguous than prevision conventions. If I understand correctly, the paper defines explicit regularization as mechanism that is explicitly designed to reduce the model capacity, and implicit one as anything else (that happens to improve generalization). There is still a lot ambiguity here: for example, one technique, say, random left-right flip of the inputs, is considered implicit regularization because it improves CIFAR-10 classification performance. However, applying it to wrong data, e.g. speech recognition, would potentially hurt the test performance. Would this technique sometimes be an implicit regularization and sometimes not? Another question is regarding dropout, which is classified as explicit regularization by the paper. Yet, if dropout is applied not to some intermediate layer, but to the input layer, does it suddenly become more similar to data augmentation (think of, e.g. cutout augmentation), and therefore an *implicit* regularization? I think a if clear, mathematical and formally verifiable definition of different types of regularization, if possible, will definite make the paper stronger.
2. Theoretical analysis: currently the paper shows the definition of the Rademacher complexity and some handwavy discussions. It does not provide any additional insights apart from what the definition says: explicit regularization constrains the capacity and data augmentation increase the number of training examples. The discussion mentioned that the augmented data are non i.i.d. which would be an interesting topic for providing theoretical insights, but it is ruled "out of the scope of the paper". Besides, the definition of the Rademacher complexity captures the capacity of the hypothesis via the mathematical sup operator, and is completely independent of the underlying algorithm used to compute the sup. Therefore, data augmentation, which is part of the training procedure, does not change the Rademacher complexity, unless we completely re-define the hypothesis space to be a complicated notion that captures something like "all the functions achievable by SGD with this augmentation and that hyper-parameters, etc.". But this will turn the "implicit regularization" nature of "data augmentation" into the regime of "explicit regularization", because it is now confining the hypothesis space into a subset (so reducing the capacity).
In summary, data augmentation clearly improves the generalization performance, but to formally characterize it, one needs an alternative approach from the default Rademacher complexity way. It would make the paper much stronger if a viable approach is proposed, and even stronger if the non-i.i.d. nature of augmented data points could be discussed under the proposed framework.
3. Empirical studies: I think the paper could still be strong even if without any theoretical characterization, if it contains strong supporting experiments. However, currently the paper only studies image classification tasks and a few convolutional neural networks. Moreover, even within those domain and tasks, there are many well known techniques, such as the cutout augmentation, that are not studied. It would be of a much useful paper to the community if the paper could provide a comprehensive survey of existing explicit / implicit regularization techniques across multiple tasks and domains (e.g. the paper mentioned vision, speech recognition, NLP, etc.). Ideally, pros and cons of each regularization technique could be discussed and if the message that "data augmentation is better than explicit regularization" could hold across multiple domains and tasks, then this will definitely be delivering a strong message. Even if that is not the case, this paper could still serve as practical guides to practitioners for choosing between different approaches of regularization techniques. 
Other potential improvements are:
a. hyper-parameter tuning for each regularization technique. The paper acknowledged that the hyper-parameters are suboptimal when they change settings for explicit regularization. This is demonstrating a good point that explicit regularization is inconvenient. However, it would be better if in parallel with the default hyper-parameter, we could learn what would be the best results if we re-tune the best hyper-parameters. Because sometimes people would like to achieve the best performance in practice with all the resources and hammers they could get their hands on. So, for example, with strong data augmentation, would the (re-tuned) optimal dropout rate actually perform even better? (if true, this is also contradicting with the main message of the paper)
b. to some extent data augmentation also have "hyper-parameters", which control how to augment the data. It would be interesting to see, for example, controlled experiments on the effects on test performance when the "wrong" type or magnitudes of augmentation is applied to the data. 
minor things:
* all bars in the plots are red / purplish colors. Maybe more diverse colors could be used to make it easier to distinguish which is which?
* Why is there no DenseNet results on ImageNet? (e.g. Figure 1 missing one row?)
* As a paper that studies augmentation, it would be great to provide full details on all the details (including the hyper parameters on the magnitudes of all perturbations) of the data augmentation used, especially for the "heavier augmentation" variant.
Finally, I'm not sure if this violate the policy of anonymous submission --- the acknowledgements section mentioned explicit names when thanking for feedback.