The task of few-shot GAN adaptation aims to adapt a pre-trained GAN model to a small dataset with very few training images. While existing methods perform well when the dataset for pre-training is structurally similar to the target dataset, the approaches suffer from training instabilities or memorization issues when the objects in the two domains have a very different structure. To mitigate this limitation, we propose a new smoothness similarity regularization that transfers the inherently learned smoothness of the pre-trained GAN to the few-shot target domain even if the two domains are very different. We evaluate our approach by adapting an unconditional and a class-conditional GAN to diverse few-shot target domains. Our proposed method significantly outperforms prior few-shot GAN adaptation methods in the challenging case of structurally dissimilar source-target domains, while performing on par with the state of the art for similar source-target domains.

我们提出了一种新的平滑相似正则化方法，将预训练的 GAN 的平滑性从结构上不同的领域转移到少样本目标域，解决了现有方法在结构不同的源域和目标域之间容易出现训练不稳定性和记忆问题的限制，实验证明我们的方法在结构上不同的源目标域的情况下，显著优于现有的少样本 GAN 适应方法，而在结构相似的源目标域方面与现有技术达到相同水平。

少样本GAN适应的光滑相似性正则化