Pruning the parameters of deep neural networks has generated intense interest
due to potential savings in time, memory and energy both during training and at
test time. Recent works have identified, through an expensive sequence of
training and pruning cycles, the existence of winning lottery tickets or sparse
trainable subnetworks at initialization. This raises a foundational question:
can we identify highly sparse trainable subnetworks at initialization, without
ever training, or indeed without ever looking at the data? We provide an
affirmative answer to this question through theory driven algorithm design. We
first mathematically formulate and experimentally verify a conservation law
that explains why existing gradient-based pruning algorithms at initialization
suffer from layer-collapse, the premature pruning of an entire layer rendering
a network untrainable. This theory also elucidates how layer-collapse can be
entirely avoided, motivating a novel pruning algorithm Iterative Synaptic Flow
Pruning (SynFlow). This algorithm can be interpreted as preserving the total
flow of synaptic strengths through the network at initialization subject to a
sparsity constraint. Notably, this algorithm makes no reference to the training
data and consistently competes with or outperforms existing state-of-the-art
pruning algorithms at initialization over a range of models (VGG and ResNet),
datasets (CIFAR-10/100 and Tiny ImageNet), and sparsity constraints (up to
99.99 percent). Thus our data-agnostic pruning algorithm challenges the
existing paradigm that, at initialization, data must be used to quantify which
synapses are important.

我们提出了一种不需要训练数据也能够在初始阶段识别高度稀疏的可训练子网络的剪枝算法，该算法基于神经突触流动原理并叫做迭代神经突触流剪枝算法（SynFlow），在多种模型、数据集和稀疏约束条件下，表现出和现有最先进的基于梯度的剪枝算法相当或更好的结果，成功挑战了基于训练数据来定量判断哪些突触重要的传统范式。