Network pruning is a promising way to generate light but accurate models and
enable their deployment on resource-limited edge devices. However, the current
state-of-the-art assumes that the effective sub-network and the other
superfluous parameters in the given network share the same distribution, where
pruning inevitably involves a distribution truncation operation. They usually
eliminate values near zero. While simple, it may not be the most appropriate
method, as effective models may naturally have many small values associated
with them. Removing near-zero values already embedded in model space may
significantly reduce model accuracy. Another line of work has proposed to
assign discrete prior over all possible sub-structures that still rely on
human-crafted prior hypotheses. Worse still, existing methods use regularized
point estimates, namely Hard Pruning, that can not provide error estimations
and fail reliability justification for the pruned networks. In this paper, we
propose a novel distribution-lossless pruning method, named DLLP, to
theoretically find the pruned lottery within Bayesian treatment. Specifically,
DLLP remodels the vanilla networks as discrete priors for the latent pruned
model and the other redundancy. More importantly, DLLP uses Stein Variational
Inference to approach the latent prior and effectively bypasses calculating KL
divergence with unknown distribution. Extensive experiments based on small
Cifar-10 and large-scaled ImageNet demonstrate that our method can obtain
sparser networks with great generalization performance while providing
quantified reliability for the pruned model.

本文提出了一种名为 DLLP 的新型网络剪枝方法，使用 Stein 变分推理和蒙特卡罗采样来避免对未知分布计算 KL 散度，该方法可以在不损失模型精度的情况下削减模型大小并提高通用性能。