  The lottery ticket hypothesis suggests that sparse, sub-networks of a given
neural network, if initialized properly, can be trained to reach comparable or
even better performance to that of the original network. Prior works in lottery
tickets have primarily focused on the supervised learning setup, with several
papers proposing effective ways of finding "winning tickets" in classification
problems. In this paper, we confirm the existence of winning tickets in deep
generative models such as GANs and VAEs. We show that the popular iterative
magnitude pruning approach (with late rewinding) can be used with generative
losses to find the winning tickets. This approach effectively yields tickets
with sparsity up to 99% for AutoEncoders, 93% for VAEs and 89% for GANs on
CIFAR and Celeb-A datasets. We also demonstrate the transferability of winning
tickets across different generative models (GANs and VAEs) sharing the same
architecture, suggesting that winning tickets have inductive biases that could
help train a wide range of deep generative models. Furthermore, we show the
practical benefits of lottery tickets in generative models by detecting tickets
at very early stages in training called "early-bird tickets". Through
early-bird tickets, we can achieve up to 88% reduction in floating-point
operations (FLOPs) and 54% reduction in training time, making it possible to
train large-scale generative models over tight resource constraints. These
results out-perform existing early pruning methods like SNIP (Lee, Ajanthan,
and Torr 2019) and GraSP (Wang, Zhang, and Grosse 2020). Our findings shed
light towards existence of proper network initializations that could improve
convergence and stability of generative models.
