The success of iterative pruning methods in achieving state-of-the-art sparse
networks has largely been attributed to improved mask identification and an
implicit regularization induced by pruning. We challenge this hypothesis and
instead posit that their repeated cyclic training schedules enable improved
optimization. To verify this, we show that pruning at initialization is
significantly boosted by repeated cyclic training, even outperforming standard
iterative pruning methods. The dominant mechanism how this is achieved, as we
conjecture, can be attributed to a better exploration of the loss landscape
leading to a lower training loss. However, at high sparsity, repeated cyclic
training alone is not enough for competitive performance. A strong coupling
between learnt parameter initialization and mask seems to be required. Standard
methods obtain this coupling via expensive pruning-training iterations,
starting from a dense network. To achieve this with sparse training instead, we
propose SCULPT-ing, i.e., repeated cyclic training of any sparse mask followed
by a single pruning step to couple the parameters and the mask, which is able
to match the performance of state-of-the-art iterative pruning methods in the
high sparsity regime at reduced computational cost.

通过重复周期性训练，我们提出了 SCULPT-ing 方法，即通过稀疏掩膜的重复周期性训练，然后进行单次剪枝步骤以耦合参数和掩膜，从而在减少计算成本的同时，在高稀疏度条件下达到与最先进的迭代剪枝方法相匹配的性能。