Mind the Gap in Distilling StyleGANs. (arXiv:2208.08840v1 [cs.CV])

StyleGAN family is one of the most popular Generative Adversarial Networks
(GANs) for unconditional generation. Despite its impressive performance, its
high demand on storage and computation impedes their deployment on
resource-constrained devices. This paper provides a comprehensive study of
distilling from the popular StyleGAN-like architecture. Our key insight is that
the main challenge of StyleGAN distillation lies in the output discrepancy
issue, where the teacher and student model yield different outputs given the
same input latent code. Standard knowledge distillation …

arxiv cv gap