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Dynamic Sparse Training (DST) is a rapidly evolving area of research thatseeks to optimize the sparse initialization of a neural network by adapting itstopology during training. It has been shown that under specific conditions, DSTis able to outperform dense models. The key components of this framework arethe pruning and growing criteria, which are repeatedly applied during thetraining process to adjust the network's sparse connectivity. While the growingcriterion's impact on DST performance is relatively well studied, the influenceof the pruning criterion remains overlooked. To address this issue, we designand perform an extensive empirical analysis of various pruning criteria tobetter understand their impact on the dynamics of DST solutions. Surprisingly,we find that most of the studied methods yield similar results. The differencesbecome more significant in the low-density regime, where the best performanceis predominantly given by the simplest technique: magnitude-based pruning. Thecode is provided at https://github.com/alooow/fantastic_weights_paper

Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training