In this paper, we address the challenge of compressing generative adversarial
networks (GANs) for deployment in resource-constrained environments by
proposing two novel methodologies: Distribution Matching for Efficient
compression (DiME) and Network Interactive Compression via Knowledge Exchange
and Learning (NICKEL). DiME employs foundation models as embedding kernels for
efficient distribution matching, leveraging maximum mean discrepancy to
facilitate effective knowledge distillation. Simultaneously, NICKEL employs an
interactive compression method that enhances the communication between the
student generator and discriminator, achieving a balanced and stable
compression process. Our comprehensive evaluation on the StyleGAN2 architecture
with the FFHQ dataset shows the effectiveness of our approach, with NICKEL &
DiME achieving FID scores of 10.45 and 15.93 at compression rates of 95.73% and
98.92%, respectively. Remarkably, our methods sustain generative quality even
at an extreme compression rate of 99.69%, surpassing the previous
state-of-the-art performance by a large margin. These findings not only
demonstrate our methodologies' capacity to significantly lower GANs'
computational demands but also pave the way for deploying high-quality GAN
models in settings with limited resources. Our code will be released soon.

本文提出了两种新方法：DiME 和 NICKEL，用于在资源受限环境中压缩生成对抗网络（GANs），这些方法能够有效地减少 GANs 的计算需求，并在压缩率极高的情况下仍保持生成质量。