Neural network pruning is a fruitful area of research with surging interest
in high sparsity regimes. Benchmarking in this domain heavily relies on
faithful representation of the sparsity of subnetworks, which has been
traditionally computed as the fraction of removed connections (direct
sparsity). This definition, however, fails to recognize unpruned parameters
that detached from input or output layers of underlying subnetworks,
potentially underestimating actual effective sparsity: the fraction of
inactivated connections. While this effect might be negligible for moderately
pruned networks (up to 10-100 compression rates), we find that it plays an
increasing role for thinner subnetworks, greatly distorting comparison between
different pruning algorithms. For example, we show that effective compression
of a randomly pruned LeNet-300-100 can be orders of magnitude larger than its
direct counterpart, while no discrepancy is ever observed when using SynFlow
for pruning [Tanaka et al., 2020]. In this work, we adopt the lens of effective
sparsity to reevaluate several recent pruning algorithms on common benchmark
architectures (e.g., LeNet-300-100, VGG-19, ResNet-18) and discover that their
absolute and relative performance changes dramatically in this new and more
appropriate framework. To aim for effective, rather than direct, sparsity, we
develop a low-cost extension to most pruning algorithms. Further, equipped with
effective sparsity as a reference frame, we partially reconfirm that random
pruning with appropriate sparsity allocation across layers performs as well or
better than more sophisticated algorithms for pruning at initialization [Su et
al., 2020]. In response to this observation, using a simple analogy of pressure
distribution in coupled cylinders from physics, we design novel layerwise
sparsity quotas that outperform all existing baselines in the context of random
pruning.

本文针对神经网络剪枝技术在高稀疏度领域的应用，提出了新的有效稀疏度概念，重定义了性能评价指标，同时开发了一个成本较低的扩展工具，通过评估各种剪枝算法的绝对和相对性能表现，证明新的评价框架下与基于初始化的剪枝算法相比，随机剪枝仍然是一种可行的方法。