  Generative adversarial networks (GANs) have received an upsurging interest
since being proposed due to the high quality of the generated data. While
achieving increasingly impressive results, the resource demands associated with
the large model size hinders the usage of GANs in resource-limited scenarios.
For inference, the existing model compression techniques can reduce the model
complexity with comparable performance. However, the training efficiency of
GANs has less been explored due to the fragile training process of GANs. In
this paper, we, for the first time, explore the possibility of directly
training sparse GAN from scratch without involving any dense or pre-training
steps. Even more unconventionally, our proposed method enables directly
training sparse unbalanced GANs with an extremely sparse generator from
scratch. Instead of training full GANs, we start with sparse GANs and
dynamically explore the parameter space spanned over the generator throughout
training. Such a sparse-to-sparse training procedure enhances the capacity of
the highly sparse generator progressively while sticking to a fixed small
parameter budget with appealing training and inference efficiency gains.
Extensive experiments with modern GAN architectures validate the effectiveness
of our method. Our sparsified GANs, trained from scratch in one single run, are
able to outperform the ones learned by expensive iterative pruning and
re-training. Perhaps most importantly, we find instead of inheriting parameters
from expensive pre-trained GANs, directly training sparse GANs from scratch can
be a much more efficient solution. For example, only training with a 80% sparse
generator and a 70% sparse discriminator, our method can achieve even better
performance than the dense BigGAN.
