Regularization has long been utilized to learn sparsity in deep neural
network pruning. However, its role is mainly explored in the small penalty
strength regime. In this work, we extend its application to a new scenario
where the regularization grows large gradually to tackle two central problems
of pruning: pruning schedule and weight importance scoring. (1) The former
topic is newly brought up in this work, which we find critical to the pruning
performance while receives little research attention. Specifically, we propose
an L2 regularization variant with rising penalty factors and show it can bring
significant accuracy gains compared with its one-shot counterpart, even when
the same weights are removed. (2) The growing penalty scheme also brings us an
approach to exploit the Hessian information for more accurate pruning without
knowing their specific values, thus not bothered by the common Hessian
approximation problems. Empirically, the proposed algorithms are easy to
implement and scalable to large datasets and networks in both structured and
unstructured pruning. Their effectiveness is demonstrated with modern deep
neural networks on the CIFAR and ImageNet datasets, achieving competitive
results compared to many state-of-the-art algorithms. Our code and trained
models are publicly available at
this https URL

本文提出了一种新的方法，将正则化引入到神经网络的剪枝问题中，并提出了一种增量 L2 规范化变量的方法来解决剪枝安排和权重重要性评分的问题。这种增量非常成功，使得我们的算法在结构化和非结构化的剪枝条件下都具有可扩展性，并在 CIFAR 和 ImageNet 数据集上取得了与许多现有算法相媲美的结果。