The recent lottery ticket hypothesis proposes that there is one sub-network that matches the accuracy of the original network when trained in isolation. We show that instead each network contains several winning tickets, even if the initial weights are fixed. The resulting winning sub-networks are not instances of the same network under weight space symmetry, and show no overlap or correlation significantly larger than expected by chance. If randomness during training is decreased, overlaps higher than chance occur, even if the networks are trained on different tasks. We conclude that there is rather a distribution over capable sub-networks, as opposed to a single winning ticket.

翻译：最近的彩票假设提出,在单独培训时,有一个子网络与原始网络的准确性相匹配。 我们显示,尽管初始重量已经固定,但每个网络都包含几张中奖票。 结果,获胜的子网络并不是在重量空间对称下属于同一个网络,没有出现比偶然预期大得多的重叠或相关关系。 如果培训中的随机性减少,即使网络接受不同任务的培训,重叠也比偶然性高。 我们的结论是,对有能力的子网络进行分配,而不是单张中奖票。