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One of the biggest issues in deep learning theory is the generalizationability of networks with huge model size. The classical learning theorysuggests that overparameterized models cause overfitting. However, practicallyused large deep models avoid overfitting, which is not well explained by theclassical 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 compressednetwork, and it is not applicable to the non-compressed original network. Inthis paper, we give a unified frame-work that can convert compression basedbounds to those for non-compressed original networks. The bound gives evenbetter rate than the one for the compressed network by improving the bias term.By establishing the unified frame-work, we can obtain a data dependentgeneralization error bound which gives a tighter evaluation than the dataindependent ones.

Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural network