Generative Adversarial Networks (GANs) have witnessed prevailing success in
yielding outstanding images, however, they are burdensome to deploy on
resource-constrained devices due to ponderous computational costs and hulking
memory usage. Although recent efforts on compressing GANs have acquired
remarkable results, they still exist potential model redundancies and can be
further compressed. To solve this issue, we propose a novel online
multi-granularity distillation (OMGD) scheme to obtain lightweight GANs, which
contributes to generating high-fidelity images with low computational demands.
We offer the first attempt to popularize single-stage online distillation for
GAN-oriented compression, where the progressively promoted teacher generator
helps to refine the discriminator-free based student generator. Complementary
teacher generators and network layers provide comprehensive and
multi-granularity concepts to enhance visual fidelity from diverse dimensions.
Experimental results on four benchmark datasets demonstrate that OMGD successes
to compress 40x MACs and 82.5X parameters on Pix2Pix and CycleGAN, without loss
of image quality. It reveals that OMGD provides a feasible solution for the
deployment of real-time image translation on resource-constrained devices. Our
code and models are made public at: this https URL

本文提出了一种新颖的在线多粒度蒸馏（OMGD）方案，该方案可用于生成低计算需求的轻量级 GAN，从而实现在资源受限设备上进行实时图像转换，并显示 OMGD 在四个基准数据集上的实验结果。