  Yes. In this paper, we investigate strong lottery tickets in generative
models, the subnetworks that achieve good generative performance without any
weight update. Neural network pruning is considered the main cornerstone of
model compression for reducing the costs of computation and memory.
Unfortunately, pruning a generative model has not been extensively explored,
and all existing pruning algorithms suffer from excessive weight-training
costs, performance degradation, limited generalizability, or complicated
training. To address these problems, we propose to find a strong lottery ticket
via moment-matching scores. Our experimental results show that the discovered
subnetwork can perform similarly or better than the trained dense model even
when only 10% of the weights remain. To the best of our knowledge, we are the
first to show the existence of strong lottery tickets in generative models and
provide an algorithm to find it stably. Our code and supplementary materials
are publicly available.
