  Recent studies assessing the efficacy of pruning neural networks methods
uncovered a surprising finding: when conducting ablation studies on existing
pruning-at-initialization methods, namely SNIP, GraSP, SynFlow, and magnitude
pruning, performances of these methods remain unchanged and sometimes even
improve when randomly shuffling the mask positions within each layer (Layerwise
Shuffling) or sampling new initial weight values (Reinit), while keeping
pruning masks the same. We attempt to understand the reason behind such network
immunity towards weight/mask modifications, by studying layer-wise statistics
before and after randomization operations. We found that under each of the
pruning-at-initialization methods, the distribution of unpruned weights changed
minimally with randomization operations.
