Paper:
One of the biggest issues in deep learning theory is the generalization ability of networks with huge model size. The classical learning theory suggests that overparameterized models cause overfitting. However, practically used large deep models avoid overfitting, which is not well explained by the classical approaches. To resolve this issue, several attempts have been made. Among them, the compression based bound is one of the promising approaches. However, the compression based bound can be applied only to a compressed network, and it is not applicable to the non-compressed original network. In this paper, we give a unified framework that can convert compression based bounds to those for non-compressed original networks. The bound gives even better rate than the one for the compressed network by improving the bias term. By establishing the unified framework, we can obtain a data dependent generalization error bound which gives a tighter evaluation than the data independent ones.

Review:
The paper presents novel theoretical results on generalization bounds via compression. Similar ideas in the last few years appeared, but only bounds on a compressed network were obtained. In contrast, the current submission gives a bound on the original (uncompressed) network in terms of the complexity of the compressed network class.
Overall, the paper seems to be well-written. I appreciate that the outlines of the proofs are included in the main text, which helps the reader follow the ideas. The result is novel and quite interesting. The new bounds seem to be still quite far from giving tight generalization theory, but I believe the paper provides some nice theoretical results for other researchers to improve upon. I think the paper could be improved immensely by some empirical analysis of the rank of compressed standard vision networks and rank of activation covariance matrices.  There are also some citation issues (see detailed comments below).
Citation issues:
In the introduction, paragraph 2, the authors cite Neyshabur et al. 2019 for the observation that networks generalize well despite being overparameterized. It seems like an odd choice. Why is Barttlet’s ‘99 paper [“Size of the weights…”]  not cited? Or at least Neyshabur et al. 2015? 
Then the authors mention that classical learning theory cannot explain the phenomena mentioned above, and classical theory “.. suggests ” that overparameterized models cause overfitting…”. The authors need to be more precise and add citations (I am assuming that the authors are talking about VC bounds for worst-case ERM generalization).
In the third paragraph, where the authors talk about norm-based bounds being lose, it seems that Nagarajan and Kolter 2019 should be cited (not only at the end), as well as Dzigaite and Roy 2017 (they look into the looseness of path-norm and margin-based bounds).
Could the authors comment more on how the bound in Theorem 2 is superior to VC dimension bound and whether conditions under which the bound is tight are realistic for standard compressed vision networks. Having weight matrices to be close to rank 1 seems unrealistic.I would like to see some sort of empirical evidence if the authors believe that this is the case. And for larger ranks, the bound seems to be close to VC bound.
In general, I found the notation a bit hard to follow and had to constantly be looking through the paper to find the definitions of various quantities. Having three different r’s, multiple mu’s with dots, bars, stars, etc., was definitely confusing and required extra attention to detail.
Other minor comments:
In section 2, marginal distributions over x and y are introduced. Are those used in the main text?
Is that a definition of \mu with the dot on top in assumption 5, or is this mu with the dot defined earlier? Using notation := would make it clearer whether the quantity is being defined.
In Section 3, “The main difference from the…” paragraph, there is \Psi(\dot r) used. Where is that defined?