Generative adversarial networks (GANs) have achieved remarkable progress in
recent years, but the continuously growing scale of models makes them
challenging to deploy widely in practical applications. In particular, for
real-time generation tasks, different devices require generators of different
sizes due to varying computing power. In this paper, we introduce slimmable
GANs (SlimGANs), which can flexibly switch the width of the generator to
accommodate various quality-efficiency trade-offs at runtime. Specifically, we
leverage multiple discriminators that share partial parameters to train the
slimmable generator. To facilitate the \textit{consistency} between generators
of different widths, we present a stepwise inplace distillation technique that
encourages narrow generators to learn from wide ones. As for class-conditional
generation, we propose a sliceable conditional batch normalization that
incorporates the label information into different widths. Our methods are
validated, both quantitatively and qualitatively, by extensive experiments and
a detailed ablation study.

本文提出了一种可以灵活调整生成器宽度以适应不同质量和效率平衡的 slimmable GANs，通过利用多个共享部分参数的判别器对生成器进行训练，并提出了一种逐步原位提纯技术以增加不同宽度之间的一致性，并通过切片条件化 BN 的方式将标签信息纳入不同的宽度中。