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The recent "Lottery Ticket Hypothesis" paper by Frankle & Carbin showed thata simple approach to creating sparse networks (keeping the large weights)results in models that are trainable from scratch, but only when starting fromthe same initial weights. The performance of these networks often exceeds theperformance of the non-sparse base model, but for reasons that were not wellunderstood. In this paper we study the three critical components of the LotteryTicket (LT) algorithm, showing that each may be varied significantly withoutimpacting the overall results. Ablating these factors leads to new insights forwhy LT networks perform as well as they do. We show why setting weights to zerois important, how signs are all you need to make the reinitialized networktrain, and why masking behaves like training. Finally, we discover theexistence of Supermasks, masks that can be applied to an untrained, randomlyinitialized network to produce a model with performance far better than chance(86% on MNIST, 41% on CIFAR-10).

Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask