The Lottery Ticket Hypothesis (LTH) states that a dense neural network model
contains a highly sparse subnetwork (i.e., winning tickets) that can achieve
even better performance than the original model when trained in isolation.
While LTH has been proved both empirically and theoretically in many works,
there still are some open issues, such as efficiency and scalability, to be
addressed. Also, the lack of open-source frameworks and consensual experimental
setting poses a challenge to future research on LTH. We, for the first time,
examine previous research and studies on LTH from different perspectives. We
also discuss issues in existing works and list potential directions for further
exploration. This survey aims to provide an in-depth look at the state of LTH
and develop a duly maintained platform to conduct experiments and compare with
the most updated baselines.

抽奖票假设（LTH）指出，一个密集的神经网络模型包含一个高度稀疏的子网络（即获奖票），当单独训练时可以实现比原始模型更好的性能。尽管 LTH 已经在许多工作中经过了经验和理论上的证明，但仍然存在一些待解决的问题，如效率和可扩展性。此调查旨在提供 LTH 研究现状的深入了解，并建立一个有序维护的平台来进行实验并与最新基准进行比较。