  Pruning on neural networks before training not only compresses the original
models, but also accelerates the network training phase, which has substantial
application value. The current work focuses on fine-grained pruning, which uses
metrics to calculate weight scores for weight screening, and extends from the
initial single-order pruning to iterative pruning. Through these works, we
argue that network pruning can be summarized as an expressive force transfer
process of weights, where the reserved weights will take on the expressive
force from the removed ones for the purpose of maintaining the performance of
original networks. In order to achieve optimal expressive force scheduling, we
propose a pruning scheme before training called Neural Network Panning which
guides expressive force transfer through multi-index and multi-process steps,
and designs a kind of panning agent based on reinforcement learning to automate
processes. Experimental results show that Panning performs better than various
available pruning before training methods.
