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We examine how recently documented, fundamental phenomena in deep learningmodels subject to pruning are affected by changes in the pruning procedure.Specifically, we analyze differences in the connectivity structure and learningdynamics of pruned models found through a set of common iterative pruningtechniques, to address questions of uniqueness of trainable, high-sparsitysub-networks, and their dependence on the chosen pruning method. Inconvolutional layers, we document the emergence of structure induced bymagnitude-based unstructured pruning in conjunction with weight rewinding thatresembles the effects of structured pruning. We also show empirical evidencethat weight stability can be automatically achieved through apposite pruningtechniques.

On Iterative Neural Network Pruning, Reinitialization, and the Similarity of Masks