  Existing high-performance deep learning models require very intensive
computing. For this reason, it is difficult to embed a deep learning model into
a system with limited resources. In this paper, we propose the novel idea of
the network compression as a method to solve this limitation. The principle of
this idea is to make iterative pruning more effective and sophisticated by
simulating the reduced network. A simple experiment was conducted to evaluate
the method; the results showed that the proposed method achieved higher
performance than existing methods at the same pruning level.
