  The recent "Lottery Ticket Hypothesis" paper by Frankle & Carbin showed that
a simple approach to creating sparse networks (keeping the large weights)
results in models that are trainable from scratch, but only when starting from
the same initial weights. The performance of these networks often exceeds the
performance of the non-sparse base model, but for reasons that were not well
understood. In this paper we study the three critical components of the Lottery
Ticket (LT) algorithm, showing that each may be varied significantly without
impacting the overall results. Ablating these factors leads to new insights for
why LT networks perform as well as they do. We show why setting weights to zero
is important, how signs are all you need to make the reinitialized network
train, and why masking behaves like training. Finally, we discover the
existence of Supermasks, masks that can be applied to an untrained, randomly
initialized network to produce a model with performance far better than chance
(86% on MNIST, 41% on CIFAR-10).
