Abstract
Network pruning is widely applied to deep CNN
models due to their heavy computation costs and
achieves high performance by keeping important
weights while removing the redundancy. Pruning
redundant weights directly may hurt global information flow, which suggests that an efficient sparse
network should take graph properties into account.
Thus, instead of paying more attention to preserving important weight, we focus on the pruned architecture itself. We propose to use graph entropy
as the measurement, which shows useful properties
to craft high-quality neural graphs and enables us to
propose efficient algorithm to construct them as the
initial network architecture. Our algorithm can be
easily implemented and deployed to different popular CNN models and achieve better trade-offs