  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 frame-work 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 frame-work, we can obtain a data dependent
generalization error bound which gives a tighter evaluation than the data
independent ones.
