  We push forward neural network compression research by exploiting a novel
challenging task of large-scale conditional generative adversarial networks
(GANs) compression. To this end, we propose a gradually shrinking GAN
(PPCD-GAN) by introducing progressive pruning residual block (PP-Res) and
class-aware distillation. The PP-Res is an extension of the conventional
residual block where each convolutional layer is followed by a learnable mask
layer to progressively prune network parameters as training proceeds. The
class-aware distillation, on the other hand, enhances the stability of training
by transferring immense knowledge from a well-trained teacher model through
instructive attention maps. We train the pruning and distillation processes
simultaneously on a well-known GAN architecture in an end-to-end manner. After
training, all redundant parameters as well as the mask layers are discarded,
yielding a lighter network while retaining the performance. We comprehensively
illustrate, on ImageNet 128x128 dataset, PPCD-GAN reduces up to 5.2x (81%)
parameters against state-of-the-arts while keeping better performance.
