Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme
2021
Recently, a series of algorithms have been explored for GAN compression,
which aims to reduce tremendous computational overhead and memory usages when
deploying GANs on resource-constrained edge devices. However, most of the
existing GAN compression work only focuses on how to compress the generator,
while fails to take the discriminator into account. In this work, we revisit
the role of discriminator in GAN compression and design a novel
generator-discriminator cooperative compression scheme for GAN compression,
termed GCC. Within GCC, a selective activation discriminator automatically
selects and activates convolutional channels according to a local capacity
constraint and a global coordination constraint, which help maintain the Nash
equilibrium with the lightweight generator during the adversarial training and
avoid mode collapse. The original generator and discriminator are also
optimized from scratch, to play as a teacher model to progressively refine the
pruned generator and the selective activation discriminator. A novel online
collaborative distillation scheme is designed to take full advantage of the
intermediate feature of the teacher generator and discriminator to further
boost the performance of the lightweight generator. Extensive experiments on
various GAN-based generation tasks demonstrate the effectiveness and
generalization of GCC. Among them, GCC contributes to reducing 80%
computational costs while maintains comparable performance in image translation
tasks. Our code and models are available at \url{https://github.com/SJLeo/GCC}.
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