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We push forward neural network compression research by exploiting a novelchallenging 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) andclass-aware distillation. The PP-Res is an extension of the conventionalresidual block where each convolutional layer is followed by a learnable masklayer to progressively prune network parameters as training proceeds. Theclass-aware distillation, on the other hand, enhances the stability of trainingby transferring immense knowledge from a well-trained teacher model throughinstructive attention maps. We train the pruning and distillation processessimultaneously on a well-known GAN architecture in an end-to-end manner. Aftertraining, all redundant parameters as well as the mask layers are discarded,yielding a lighter network while retaining the performance. We comprehensivelyillustrate, on ImageNet 128x128 dataset, PPCD-GAN reduces up to 5.2x (81%)parameters against state-of-the-arts while keeping better performance.

PPCD-GAN: Progressive Pruning and Class-Aware Distillation for Large-Scale Conditional GANs Compression