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Recent work has explored the possibility of pruning neural networks atinitialization. We assess proposals for doing so: SNIP (Lee et al., 2019),GraSP (Wang et al., 2020), SynFlow (Tanaka et al., 2020), and magnitudepruning. Although these methods surpass the trivial baseline of random pruning,they remain below the accuracy of magnitude pruning after training, and weendeavor to understand why. We show that, unlike pruning after training,randomly shuffling the weights these methods prune within each layer orsampling new initial values preserves or improves accuracy. As such, theper-weight pruning decisions made by these methods can be replaced by aper-layer choice of the fraction of weights to prune. This property suggestsbroader challenges with the underlying pruning heuristics, the desire to pruneat initialization, or both.

Pruning Neural Networks at Initialization: Why are We Missing the Mark?